Статті в журналах: "Physical biases"

1

Zhang, Xuefeng, Shaoqing Zhang, Zhengyu Liu, Xinrong Wu, and Guijun Han. "Parameter Optimization in an Intermediate Coupled Climate Model with Biased Physics." Journal of Climate 28, no. 3 (February 1, 2015): 1227–47. http://dx.doi.org/10.1175/jcli-d-14-00348.1.

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Abstract Imperfect physical parameterization schemes in a coupled climate model are an important source of model biases that adversely impact climate prediction. However, how observational information should be used to optimize physical parameterizations through parameter estimation has not been fully studied. Using an intermediate coupled ocean–atmosphere model, the authors investigate parameter optimization when the assimilation model contains biased physics within a biased assimilation experiment framework. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation model and the “truth” model that is used to generate simulated observations. While the stochastic physics, implemented by initially perturbing the physical parameters, can significantly enhance the ensemble spread and improve the representation of the model ensemble, the parameter estimation is able to mitigate the model biases induced by the biased physics. Furthermore, better results for climate estimation and prediction can be obtained when only the most influential physical parameters are optimized and allowed to vary geographically. In addition, the parameter optimization with the biased model physics improves the performance of the climate estimation and prediction in the deep ocean significantly, even if there is no direct observational constraint on the low-frequency component of the state variables. These results provide some insight into decadal predictions in a coupled ocean–atmosphere general circulation model that includes imperfect physical schemes that are initialized from the climate observing system.

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Kishimoto, Reiki, Kyoshiro Sasaki, Akihiko Gobara, Yuko Ojiro, Giyeon Nam, Kayo Miura, and Yuki Yamada. "When a silhouette appears male: Observer’s own physical fitness governs social categorization of sexually ambiguous stimuli." Letters on Evolutionary Behavioral Science 7, no. 1 (April 1, 2016): 17–20. http://dx.doi.org/10.5178/lebs.2016.44.

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Categorization bias in social cognition is widely observed, as it is more beneficial to commit low-risk biases in order to reduce high-risk biases. Here, we hypothesized that a low capacity of self-protection leads to a biased social categorization of others as “harmful”. To examine this, silhouettes of human body, which had a sexually dimorphic cue (waist-to-hip ratio), were presented, and participants categorized the stimuli as being either “female” or “male”. Participants’ basic physical fitness was measured, and we manipulated exogenous physical load by either a heavy or a light backpack. Physical load did not significantly influence sex categorization. In contrast, only female participants tended to categorize the stimuli as “male” more, as basic physical fitness increased, suggesting that basic physical fitness, not physical load, affects sex categorization for females. Our findings shed new light on the role of fitness factors in social cognition.

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Keogh, Edmund, Catherine Dillon, George Georgiou, and Caroline Hunt. "Selective attentional biases for physical threat in physical anxiety sensitivity." Journal of Anxiety Disorders 15, no. 4 (July 2001): 299–315. http://dx.doi.org/10.1016/s0887-6185(01)00065-2.

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4

Luhring, Thomas M., Grant M. Connette, and Christopher M. Schalk. "Trap characteristics and species morphology explain size-biased sampling of two salamander species." Amphibia-Reptilia 37, no. 1 (2016): 79–89. http://dx.doi.org/10.1163/15685381-00003034.

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Demographic studies often depend on sampling techniques providing representative samples from populations. However, the sequence of events leading up to a successful capture or detection is susceptible to biases introduced through individual-level behaviour or physiology. Passive sampling techniques may be especially prone to sampling bias caused by size-related phenomena (e.g., physical limitations on trap entrance). We tested for size-biased sampling among five types of passive traps using a 9-year data set for two species of aquatic salamanders that have a 20 and 61 fold change in length over their ontogeny (Amphiuma means, Siren lacertina). Size-biased trapping was evident for both species, with body size distributions (body length mean and SD) of captured individuals differing among sampling techniques. Because our two species differed in girth at similar lengths, we were able to show that size biases (in length) were most likely caused by girth limitations on trap entry rates, and potentially by differences in retention rates. Accounting for the biases of sampling techniques may be critical when assessing current population status and demographic change.

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5

Bellprat, O., S. Kotlarski, D. Lüthi, and C. Schär. "Physical constraints for temperature biases in climate models." Geophysical Research Letters 40, no. 15 (August 7, 2013): 4042–47. http://dx.doi.org/10.1002/grl.50737.

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6

Arenou, Frédéric, and Xavier Luri. "Statistical Effects from Hipparcos Astrometry." Highlights of Astronomy 12 (2002): 661–64. http://dx.doi.org/10.1017/s153929960001460x.

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AbstractThe Hipparcos astrometry is used mainly for the derivation of stellar physical quantities such as luminosity, masses and velocity. However, sample selections on data with observational errors or an intrinsic dispersion may lead to biased estimates, especially when the error distributions are non-Gaussian. We review the classical biases and the ways to avoid them through the use of statistical methods.

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7

Schwemmer, Carsten, Carly Knight, Emily D. Bello-Pardo, Stan Oklobdzija, Martijn Schoonvelde, and Jeffrey W. Lockhart. "Diagnosing Gender Bias in Image Recognition Systems." Socius: Sociological Research for a Dynamic World 6 (January 2020): 237802312096717. http://dx.doi.org/10.1177/2378023120967171.

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Image recognition systems offer the promise to learn from images at scale without requiring expert knowledge. However, past research suggests that machine learning systems often produce biased output. In this article, we evaluate potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians. Our crowdsourced validation shows that commercial image recognition systems can produce labels that are correct and biased at the same time as they selectively report a subset of many possible true labels. We find that images of women received three times more annotations related to physical appearance. Moreover, women in images are recognized at substantially lower rates in comparison with men. We discuss how encoded biases such as these affect the visibility of women, reinforce harmful gender stereotypes, and limit the validity of the insights that can be gathered from such data.

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Corredera, Alberto, Marta Romero, and Jose M. Moya. "Emotional Decision-Making Biases Prediction in Cyber-Physical Systems." Big Data and Cognitive Computing 3, no. 3 (August 30, 2019): 49. http://dx.doi.org/10.3390/bdcc3030049.

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This article faces the challenge of discovering the trends in decision-making based on capturing emotional data and the influence of the possible external stimuli. We conducted an experiment with a significant sample of the workforce and used machine-learning techniques to model the decision-making process. We studied the trends introduced by the emotional status and the external stimulus that makes these personnel act or report to the supervisor. The main result of this study is the production of a model capable of predicting the bias to act in a specific context. We studied the relationship between emotions and the probability of acting or correcting the system. The main area of interest of these issues is the ability to influence in advance the personnel to make their work more efficient and productive. This would be a whole new line of research for the future.

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9

Er, Xinzhong, Junqiang Ge, and Shude Mao. "BIASES IN PHYSICAL PARAMETER ESTIMATES THROUGH DIFFERENTIAL LENSING MAGNIFICATION." Astrophysical Journal 770, no. 2 (May 30, 2013): 110. http://dx.doi.org/10.1088/0004-637x/770/2/110.

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10

Nicklin, Jessica M., and Sylvia G. Roch. "Biases Influencing Recommendation Letter Contents: Physical Attractiveness and Gender1." Journal of Applied Social Psychology 38, no. 12 (December 2008): 3053–74. http://dx.doi.org/10.1111/j.1559-1816.2008.00425.x.

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11

Smith, K., and E. Vul. "Physical prediction biases are faithful physics plus visual uncertainty." Journal of Vision 13, no. 9 (July 25, 2013): 777. http://dx.doi.org/10.1167/13.9.777.

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12

Taylor, Alisdair James Gordon, and Maria Jose. "Physical Aggression and Facial Expression Identification." Europe’s Journal of Psychology 10, no. 4 (November 28, 2014): 650–59. http://dx.doi.org/10.5964/ejop.v10i4.816.

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Social information processing theories suggest that aggressive individuals may exhibit hostile perceptual biases when interpreting other’s behaviour. This hypothesis was tested in the present study which investigated the effects of physical aggression on facial expression identification in a sample of healthy participants. Participants were asked to judge the expressions of faces presented to them and to complete a self-report measure of aggression. Relative to low physically aggressive participants, high physically aggressive participants were more likely to mistake non-angry facial expressions as being angry facial expressions (misattribution errors), supporting the idea of a hostile predisposition. These differences were not explained by gender, or response times. There were no differences in identifying angry expressions in general between aggression groups (misperceived errors). These findings add support to the idea that aggressive individuals exhibit hostile perceptual biases when interpreting facial expressions.

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13

Chakraborty, Rahul, Amy Louise Schwarz, and Phillip Vaughan. "Speech-Language Pathologists' Perceptions of Nonnative Accent: A Pilot Study." Perspectives of the ASHA Special Interest Groups 4, no. 6 (December 26, 2019): 1601–11. http://dx.doi.org/10.1044/2019_pers-sig17-2019-0030.

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Purpose Considering the American Speech-Language-Hearing Association's (ASHA) global influence and continued emphasis on heightening multicultural receptivity, a major consideration for ASHA is to cultivate cultural sensitivity and competence in its largely female (95.30%), monolingual (93.46%), and White (92.10%) workforce. ASHA's 32-year transition from no formal multicultural education to mandatory multicultural education infusion provokes to enquire whether multicultural education in speech-language pathologists (SLPs) has modified SLPs' perceptions toward multicultural issues. In this article, we explored an aspect of such sensitivity. We want to know whether there exists a linear relationship between SLPs' age, as an index of their exposure to formal multicultural education, and SLPs' bias toward nonnative accent, as an index of their multicultural sensitivity. Method Two hundred sixty-eight SLP ASHA members completed a web-based pilot survey testing whether SLPs made biased associations based on personality traits, sociocultural factors, professional attire, and physical appearance of persons with nonnative accent and whether the SLPs thought they had an accent. Logistic regression tested whether age predicts explicit biased associations for these five outcomes. Results ASHA SLPs made biased associations with accent for all outcomes, but age only predicted the likelihood of explicit accent-based biases for physical appearance. The relationship was curvilinear, with 40- to 45-year-olds making the most biased associations and older SLPs making progressively fewer biased associations. Conclusion Clearly, the specific psychosocial attributes associated with perception of nonnative accent need to be explored from a dynamical systems perspective to discover potential variables that might directly or indirectly influence accent perception and potential biases.

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14

Biswas, Soumyajyoti, F. W. S. Lima, and Ophir Flomenbom. "Are Socio-Econo-Physical Models Better to Explain Biases in Societies?" Reports in Advances of Physical Sciences 02, no. 02 (June 2018): 1850006. http://dx.doi.org/10.1142/s2424942418500068.

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In this work, we review various dynamical models in socio-econo-physics, and discuss their roles in understanding socio-economic biases. We deal with fairness in nations, wealth distributions in nations, elections’ dynamics and tax evasion models. We claim that these models can better explain biases in societies than traditional models of economics from 1700s that are based on various assumptions such as the “invisible hand concept” that justifies interventions from social beneficiaries.

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15

Li, Xiutian, Siqi Sun, and Rui Feng. "Causal Representation Learning via Counterfactual Intervention." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (March 24, 2024): 3234–42. http://dx.doi.org/10.1609/aaai.v38i4.28108.

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Existing causal representation learning methods are based on the causal graph they build. However, due to the omission of bias within the causal graph, they essentially encourage models to learn biased causal effects in latent space. In this paper, we propose a novel causally disentangling framework that aims to learn unbiased causal effects. We first introduce inductive and dataset biases into traditional causal graph for the physical concepts of interest. Then, we eliminate the negative effects from these two biases by counterfactual intervention with reweighted loss function for learning unbiased causal effects. Finally, we employ the causal effects into the VAE to endow the latent representations with causality. In particular, we highlight that removing biases in this paper is regarded as a part of learning process for unbiased causal effects, which is crucial for causal disentanglement performance improvement. Through extensive experiments on real-world and synthetic datasets, we show that our method outperforms different baselines and obtains the state-of-the-art results for achieving causal representation learning.

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16

KASPAR, Kai, and Christoph JAHN. "PHYSICAL CLEANSING BIASES RECOGNITION PERFORMANCE FOR (IM)MORAL SOCIAL ISSUES." Studia Psychologica 58, no. 4 (2016): 307–21. http://dx.doi.org/10.21909/sp.2016.04.725.

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Mouratidou, Katerina, and Vassilis Barkoukis. "Greek Physical Education Teachers’ Gender Biases in Learning and Teaching." Physical Educator 75, no. 2 (2018): 210–28. http://dx.doi.org/10.18666/tpe-2018-v75-i2-7105.

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18

Göksun, Tilbe, Adam J. Woods, Anjan Chatterjee, Sarah Zelonis, Leila Glass, and Sabrina E. Smith. "Elementary school children’s attentional biases in physical and numerical space." European Journal of Developmental Psychology 10, no. 4 (July 2013): 433–48. http://dx.doi.org/10.1080/17405629.2012.692965.

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19

Willis, Josh K., John M. Lyman, Gregory C. Johnson, and John Gilson. "In Situ Data Biases and Recent Ocean Heat Content Variability*." Journal of Atmospheric and Oceanic Technology 26, no. 4 (April 1, 2009): 846–52. http://dx.doi.org/10.1175/2008jtecho608.1.

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Abstract Two significant instrument biases have been identified in the in situ profile data used to estimate globally integrated upper-ocean heat content. A large cold bias was discovered in a small fraction of Argo floats along with a smaller but more prevalent warm bias in expendable bathythermograph (XBT) data. These biases appear to have caused the bulk of the upper-ocean cooling signal reported by Lyman et al. between 2003 and 2005. These systematic data errors are significantly larger than sampling errors in recent years and are the dominant sources of error in recent estimates of globally integrated upper-ocean heat content variability. The bias in the XBT data is found to be consistent with errors in the fall-rate equations, suggesting a physical explanation for that bias. With biased profiles discarded, no significant warming or cooling is observed in upper-ocean heat content between 2003 and 2006.

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20

Vaimberg, Emma, Lindsay Demers, Eric Ford, Maya Sabatello, Blair Stevens, and Shoumita Dasgupta. "Project Inclusive Genetics: Exploring the impact of patient-centered counseling training on physical disability bias in the prenatal setting." PLOS ONE 16, no. 8 (August 5, 2021): e0255722. http://dx.doi.org/10.1371/journal.pone.0255722.

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Purpose There is robust research examining the negative impact of racial and socioeconomic implicit bias on healthcare provider clinical decision-making. However, other under-studied important biases are likely to impact clinical care as well. The goal of this study was to explore the presence of bias against people with physical disability among a heterogeneous group of healthcare workers and trainees and to evaluate the effect of implicit association testing and an educational module on this bias. Method The study was composed of a one-hour web-based survey and educational module. The survey included an explicit disability bias assessment, disability Implicit Association Tests (IATs), demographic collection, and pre- and post- module clinical vignettes of prenatal patient scenarios. In addition to providing counseling to hypothetical patients, participants also indicated their personal preferences on genetic testing and termination. The educational module focused on the principles of patient-centered counseling. Results The collected data reflects responses from 335 participants. Within this sample, there were both explicit and implicit biases towards individuals with physical disabilities. Prior to the IAT and educational module, when respondents were tasked with providing genetic testing recommendations, implicit biases and personal preferences for genetic testing and termination influenced respondents’ clinical recommendations. Importantly, having previous professional experience with individuals with disabilities diminished biased clinical recommendations prior to the intervention. In response to the IAT and educational intervention, the effect of implicit bias and personal preferences on clinical recommendations decreased. Conclusions This study demonstrates how bias against a marginalized group exists within the medical community and that personal opinions can impact clinical counseling. Importantly, our findings suggest that there are strategies that can be easily implemented into curricula to address disability bias, including formal educational interventions and the addition of professional experiences into healthcare professional training programs.

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Kirkpatrick, Sharon, Clare Collins, Ruth Keogh, Susan Krebs-Smith, Marian Neuhouser, and Angela Wallace. "Assessing Dietary Outcomes in Intervention Studies: Pitfalls, Strategies, and Research Needs." Nutrients 10, no. 8 (July 31, 2018): 1001. http://dx.doi.org/10.3390/nu10081001.

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To inform strategies to improve the dietary intakes of populations, robust evaluations of interventions are required. This paper is drawn from a workshop held at the International Society of Behavioral Nutrition and Physical Activity 2017 Annual Meeting, and highlights considerations and research priorities relevant to measuring dietary outcomes within intervention studies. Self-reported dietary data are typically relied upon in such studies, and it is recognized that these data are affected by random and systematic error. Additionally, differential error between intervention and comparison groups or pre- and post-intervention can be elicited by the intervention itself, for example, by creating greater awareness of eating or drinking occasions or the desire to appear compliant. Differential reporting can render the results of trials incorrect or inconclusive by leading to biased estimates and reduced statistical power. The development of strategies to address intervention-related biases requires developing a better understanding of the situations and population groups in which interventions are likely to elicit differential reporting and the extent of the bias. Also needed are efforts to expand the feasibility and applications of biomarkers to address intervention-related biases. In the meantime, researchers are encouraged to consider the potential for differential biases in dietary reporting in a given study, to choose tools carefully and take steps to minimize and/or measure factors such as social desirability biases that might contribute to differential reporting, and to consider the implications of differential reporting for study results.

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22

Brecher, Ellyn, Jennifer Bragger, and Eugene Kutcher. "The Structured Interview: Reducing Biases Toward Job Applicants with Physical Disabilities." Employee Responsibilities and Rights Journal 18, no. 3 (November 8, 2006): 155–70. http://dx.doi.org/10.1007/s10672-006-9014-y.

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23

Sigelman, Carol K., Lee Sigelman, Dan B. Thomas, and Frederick D. Ribich. "Gender, Physical Attractiveness, and Electability: An Experimental Investigation of Voter Biases." Journal of Applied Social Psychology 16, no. 3 (May 1986): 229–48. http://dx.doi.org/10.1111/j.1559-1816.1986.tb01137.x.

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Hassan, Nida. "Gender Biases and Discrimination while Hiring." Artha - Journal of Social Sciences 18, no. 1 (January 1, 2019): 13–21. http://dx.doi.org/10.12724/ajss.48.2.

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In the employment market, hiring processes in the organisations are often considered to perpetuate gender equality. The discrimination prevails through implicit and in-group biases. During hiring decisions, marked gender differences in the hiring standards continue to exist for women and men. Counter balancing attempts, such as demonstrating agentic behaviour seem to have been unfavourable in receiving appreciations. Factors like „accumulation of advantage‟, „physical attractiveness‟ and so forth adds to the „threat‟ for the woman candidate. The article proposes that sustained and concrete processes need to be ensured that would defuse gender stereotyping, the latter being an outcome of the human tendency to carve the world into in-group and out-group. Two methods to neutralise gender biases while hiring are recommended in the concluding section.

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Fasczewski, Kimberly S., Paige N. Bramblett, Sara M. Powell, Jennifer D. Thornton-Brooks, and Nolasco R. Stevens. "Understanding Motivation for Physical Activity Charity Event Participation." American Journal of Health Behavior 45, no. 4 (July 26, 2021): 723–34. http://dx.doi.org/10.5993/ajhb.45.4.11.

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Objectives: The positive health benefits of regular physical activity (PA) are widely known, yet PA levels remain low in the general population and those with neurological conditions like multiple sclerosis (MS). The goal of this study was to use the lens of behavioral economics to explore PA motivation for participation in PA-based MS charity fundraising events to elucidate the relationship between behavioral economics constructs and motivation. Methods: Mixed-methods survey data were collected from 114 individuals (47 diagnosed with MS; 67 without MS) who had participated in a PA-based MS charity fundraiser event. Results: Quantitative data showed 23 (41.8%) of participants without MS, and 16 (43.2%) of participants living with MS cited fundraising for MS as the primary reason for participation. Open-ended responses revealed behavioral economic constructs of framing, social support, and individual positive biases were key factors contributing to increased motivation for participation. Conclusion: Habit formation for PA behaviors may emerge due to personal biases (perceived importance of fundraising) and a sense of relatedness. Involvement in MS charity events may alter social norms, frame events as enjoyable, and foster a sense of community, thereby increasing the likelihood of continued participation

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LIU, Xiying, Jiahua ZHAO, Huasheng XIA, Tonggui BAI, and Tao ZHANG. "Temperature biases in modeled polar climate and adoption of physical parameterization schemes." ADVANCES IN POLAR SCIENCE 23, no. 1 (January 21, 2013): 30–40. http://dx.doi.org/10.3724/sp.j.1085.2012.00030.

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Huang, Julie Y., Joshua M. Ackerman, and John A. Bargh. "Superman to the rescue: Simulating physical invulnerability attenuates exclusion-related interpersonal biases." Journal of Experimental Social Psychology 49, no. 3 (May 2013): 349–54. http://dx.doi.org/10.1016/j.jesp.2012.12.007.

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Zhang, Ziwen, Martin Kilbinger, Fabian Hervas Peters, Qinxun Li, Wentao Luo, Lucie Baumont, Jean-Charles Cuillandre, et al. "Point spread function errors for weak lensing – density cross-correlations." Astronomy & Astrophysics 691 (October 30, 2024): A75. http://dx.doi.org/10.1051/0004-6361/202450623.

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Aims. Calibrating the point spread function (PSF) is a fundamental part of weak gravitational lensing analyses. Even with corrected galaxy images, imperfect calibrations can introduce biases. We propose an analytical framework for quantifying PSF-induced systematics as diagnostics for cross-correlation measurements of weak lensing with density tracers; for example, galaxy-galaxy lensing. We show how those systematics propagate to physical parameters of the density tracers. Those diagnostics only require a shape catalog of PSF stars and foreground galaxy positions. Methods. We considered the PSF-induced multiplicative bias, and introduced three second-order statistics as additive biases. We computed both biases for the weak-lensing derived halo mass of spectroscopic foreground galaxy samples; in particular, their effect on the tangential shear and fit halo mass as a function of stellar mass. In addition, we assessed their impact on the recently published black-hole – halo-mass relation for type I active galactic nuclei (AGNs). Results. Using weak-lensing catalogs from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) and the Dark Energy Survey (DES), we find the multiplicative biases in the tangential shear to be less than 0.5%. No correlations between additive bias and galaxy properties of the foreground sample are detected. The combined PSF systematics affect low-mass galaxies and small angular scales; halo mass estimates can be biased by up to 18% for a sample of central galaxies in the stellar mass range of 9.0 ≤ log M*/M⊙ < 9.5. Conclusions. The PSF-induced multiplicative bias is a subdominant contribution to current studies of weak-lensing – density cross-correlations, but might become significant for upcoming stage IV surveys. For samples with a low tangential shear, additive PSF systematics can induce a significant bias on derived properties such as the halo mass.

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Metivier, Jean-Christophe, and Frédéric J. J. Chain. "Diversity in Expression Biases of Lineage-Specific Genes During Development and Anhydrobiosis Among Tardigrade Species." Evolutionary Bioinformatics 18 (January 2022): 117693432211402. http://dx.doi.org/10.1177/11769343221140277.

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Lineage-specific genes can contribute to the emergence and evolution of novel traits and adaptations. Tardigrades are animals that have adapted to tolerate extreme conditions by undergoing a form of cryptobiosis called anhydrobiosis, a physical transformation to an inactive desiccated state. While studies to understand the genetics underlying the interspecies diversity in anhydrobiotic transitions have identified tardigrade-specific genes and family expansions involved in this process, the contributions of species-specific genes to the variation in tardigrade development and cryptobiosis are less clear. We used previously published transcriptomes throughout development and anhydrobiosis (5 embryonic stages, 7 juvenile stages, active adults, and tun adults) to assess the transcriptional biases of different classes of genes between 2 tardigrade species, Hypsibius exemplaris and Ramazzottius varieornatus. We also used the transcriptomes of 2 other tardigrades, Echiniscoides sigismundi and Richtersius coronifer, and data from 3 non-tardigrade species ( Adenita vaga, Drosophila melanogaster, and Caenorhabditis elegans) to help identify lineage-specific genes. We found that lineage-specific genes have generally low and narrow expression but are enriched among biased genes in different stages of development depending on the species. Biased genes tend to be specific to early and late development, but there is little overlap in functional enrichment of biased genes between species. Gene expansions in the 2 tardigrades also involve families with different functions despite homologous genes being expressed during anhydrobiosis in both species. Our results demonstrate the interspecific variation in transcriptional contributions and biases of lineage-specific genes during development and anhydrobiosis in 2 tardigrades.

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Sellentin, Elena. "A blinding solution for inference from astronomical data." Monthly Notices of the Royal Astronomical Society 492, no. 3 (January 8, 2020): 3396–407. http://dx.doi.org/10.1093/mnras/staa043.

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ABSTRACT This paper presents a joint blinding and deblinding strategy for inference of physical laws from astronomical data. The strategy allows for up to three blinding stages, where the data may be blinded, the computations of theoretical physics may be blinded, and –assuming Gaussianly distributed data – the covariance matrix may be blinded. We found covariance blinding to be particularly effective, as it enables the blinder to determine close to exactly where the blinded posterior will peak. Accordingly, we present an algorithm which induces posterior shifts in predetermined directions by hiding untraceable biases in a covariance matrix. The associated deblinding takes the form of a numerically lightweight post-processing step, where the blinded posterior is multiplied with deblinding weights. We illustrate the blinding strategy for cosmic shear from KiDS-450, and show that even though there is no direct evidence of the KiDS-450 covariance matrix being biased, the famous cosmic shear tension with Planck could easily be induced by a mischaracterization of correlations between ξ− at the highest redshift and all lower redshifts. The blinding algorithm illustrates the increasing importance of accurate uncertainty assessment in astronomical inferences, as otherwise involuntary blinding through biases occurs.

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Shipton, Z. K., J. J. Roberts, E. L. Comrie, Y. Kremer, R. J. Lunn, and J. S. Caine. "Fault fictions: systematic biases in the conceptualization of fault-zone architecture." Geological Society, London, Special Publications 496, no. 1 (September 10, 2019): 125–43. http://dx.doi.org/10.1144/sp496-2018-161.

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AbstractMental models are a human's internal representation of the real world and have an important role in the way we understand and reason about uncertainties, explore potential options and make decisions. Mental models have not yet received much attention in geosciences, yet systematic biases can affect any geological investigation: from how the problem is conceived, through selection of appropriate hypotheses and data collection/processing methods, to the conceptualization and communication of results. We draw on findings from cognitive science and system dynamics, with knowledge and experiences of field geology, to consider the limitations and biases presented by mental models in geoscience, and their effect on predictions of the physical properties of faults in particular. We highlight biases specific to geological investigations and propose strategies for debiasing. Doing so will enhance how multiple data sources can be brought together, and minimize controllable geological uncertainty to develop more robust geological models. Critically, there is a need for standardized procedures that guard against biases, permitting data from multiple studies to be combined and communication of assumptions to be made. While we use faults to illustrate potential biases in mental models and the implications of these biases, our findings can be applied across the geosciences.

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32

Bellouin, N., W. J. Collins, I. D. Culverwell, P. R. Halloran, S. C. Hardiman, T. J. Hinton, C. D. Jones, et al. "The HadGEM2 family of Met Office Unified Model climate configurations." Geoscientific Model Development 4, no. 3 (September 7, 2011): 723–57. http://dx.doi.org/10.5194/gmd-4-723-2011.

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Abstract. We describe the HadGEM2 family of climate configurations of the Met Office Unified Model, MetUM. The concept of a model "family" comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family of configurations includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry. The HadGEM2 physical model includes improvements designed to address specific systematic errors encountered in the previous climate configuration, HadGEM1, namely Northern Hemisphere continental temperature biases and tropical sea surface temperature biases and poor variability. Targeting these biases was crucial in order that the ES configuration could represent important biogeochemical climate feedbacks. Detailed descriptions and evaluations of particular HadGEM2 family members are included in a number of other publications, and the discussion here is limited to a summary of the overall performance using a set of model metrics which compare the way in which the various configurations simulate present-day climate and its variability.

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Bellouin, N., W. J. Collins, I. D. Culverwell, P. R. Halloran, S. C. Hardiman, T. J. Hinton, C. D. Jones, et al. "The HadGEM2 family of Met Office Unified Model Climate configurations." Geoscientific Model Development Discussions 4, no. 2 (April 1, 2011): 765–841. http://dx.doi.org/10.5194/gmdd-4-765-2011.

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Abstract. We describe the HadGEM2 family of climate configurations of the Met Office Unified Model, MetUM. The concept of a model "family" comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family of configurations includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry. The HadGEM2 physical model includes improvements designed to address specific systematic errors encountered in the previous climate configuration, HadGEM1, namely Northern Hemisphere continental temperature biases and tropical sea surface temperature biases and poor variability. Targeting these biases was crucial in order that the ES configuration could represent important biogeochemical climate feedbacks. Detailed descriptions and evaluations of particular HadGEM2 family members are included in a number of other publications, and the discussion here is limited to a summary of the overall performance using a set of model metrics which compare the way in which the various configurations simulate present-day climate and its variability.

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34

Cino, Elio A., Wing-Yiu Choy, and Mikko Karttunen. "Conformational Biases of Linear Motifs." Journal of Physical Chemistry B 117, no. 50 (December 10, 2013): 15943–57. http://dx.doi.org/10.1021/jp407536p.

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35

Daalman, K., I. E. C. Sommer, E. M. Derks, and E. R. Peters. "Cognitive biases and auditory verbal hallucinations in healthy and clinical individuals." Psychological Medicine 43, no. 11 (March 1, 2013): 2339–47. http://dx.doi.org/10.1017/s0033291713000275.

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BackgroundSeveral cognitive biases are related to psychotic symptoms, including auditory verbal hallucinations (AVH). It remains unclear whether these biases differ in voice-hearers with and without a ‘need-for-care’.MethodA total of 72 healthy controls, 72 healthy voice-hearers and 72 clinical voice-hearers were compared on the Cognitive Biases Questionnaire for psychosis (CBQp), which assesses ‘intentionalizing’, ‘jumping to conclusions’, ‘catastrophizing’, ‘dichotomous thinking’ and ‘emotional reasoning’ in vignettes characterized by two themes, ‘threatening events’ and ‘anomalous perceptions’.ResultsHealthy voice-hearers scored intermediately on total CBQp between the control and clinical groups, differing significantly from both. However, on four out of five biases the scores of the healthy voice-hearers were comparable with those of the healthy controls. The only exception was ‘emotional reasoning’, on which their scores were comparable with the clinical group. Healthy voice-hearers demonstrated fewer biases than the psychotic patients on the ‘threatening events’, but not the ‘anomalous perceptions’, vignettes. CBQp scores were related to both cognitive and emotional, but not physical, characteristics of voices.ConclusionsMost cognitive biases prevalent in clinical voice-hearers, particularly with threatening events themes, are absent in healthy voice-hearers, apart from emotional reasoning which may be specifically related to the vulnerability to develop AVH. The association between biases and both beliefs about voices and distress/emotional valence is consistent with the close links between emotions and psychotic phenomena identified by cognitive models of psychosis. The absence of reasoning biases might prevent the formation of threatening appraisals about anomalous experiences, thereby reducing the likelihood of distress and ‘need for care’.

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36

Li, Linyang, Zhen Yang, Zhen Jia, and Xin Li. "Parallel Computation of Multi-GNSS and Multi-Frequency Inter-Frequency Clock Biases and Observable-Specific Biases." Remote Sensing 15, no. 7 (April 6, 2023): 1953. http://dx.doi.org/10.3390/rs15071953.

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With the widespread application of GNSS, the delicate handling of biases among different systems and different frequencies is of critical importance, wherein the inter-frequency clock biases (IFCBs) and observable-specific signal biases (OSBs) should be carefully corrected. Usually, a serial approach is used to calculate these products. To accelerate the computation speed and reduce the time delay, a multicore parallel estimation strategy for IFCBs, code, and phase OSBs by utilizing task parallel library (TPL) is proposed, the parallel computations, including precise point positioning (PPP), IFCBs, and OSBs estimation, being carried out on the basis of data parallelisms and task-based asynchronous programming. Three weeks of observables from the multi-GNSS experiment campaign (MGEX) network is utilized. The result shows that the IFCB errors of GPS Block IIF and GLONASS M+ satellites are nonnegligible, in which the GLONASS M+ satellite R21 shows the largest IFCB of more than 0.60 m, while those of other systems and frequencies are marginal, and the code OSBs present excellent stability with a standard deviation (STD) of 0.10 ns for GPS and approximately 0.20 ns for other satellite systems. Besides, the phase OSBs of all systems show the stability of better than 0.10 ns, wherein the Galileo satellites show the best performance of 0.01 ns. Compared with the single-core serial computing method, the acceleration rates for IFCBs and OSBs estimation are 3.10, 5.53, 9.66, and 17.04 times higher using four, eight, sixteen, and thirty-two physical cores, respectively, through multi-core parallelized execution.

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Short, Ewan. "Verifying Operational Forecasts of Land–Sea-Breeze and Boundary Layer Mixing Processes." Weather and Forecasting 35, no. 4 (August 1, 2020): 1427–45. http://dx.doi.org/10.1175/waf-d-19-0244.1.

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AbstractForecasters working for Australia’s Bureau of Meteorology (BoM) produce a 7-day forecast in two key steps: first they choose a model guidance dataset to base the forecast on, and then they use graphical software to manually edit these data. Two types of edits are commonly made to the wind fields that aim to improve how the influences of boundary layer mixing and land–sea-breeze processes are represented in the forecast. In this study the diurnally varying component of the BoM’s official wind forecast is compared with that of station observations and unedited model guidance datasets. Coastal locations across Australia over June, July, and August 2018 are considered, with data aggregated over three spatial scales. The edited forecast produces a lower mean absolute error than model guidance at the coarsest spatial scale (over 50 000 km2), and achieves lower seasonal biases over all spatial scales. However, the edited forecast only reduces errors or biases at particular times and locations, and rarely produces lower errors or biases than all model guidance products simultaneously. To better understand physical reasons for biases in the mean diurnal wind cycles, modified ellipses are fitted to the seasonally averaged diurnal wind temporal hodographs. Biases in the official forecast diurnal cycle vary with location for multiple reasons, including biases in the directions that sea breezes approach coastlines, amplitude biases, and disagreement in the relative contribution of sea-breeze and boundary layer mixing processes to the mean diurnal cycle.

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Sharma, Manoj. "Applying multi-theory model of health behaviour change to address implicit biases in public health." International Journal Of Community Medicine And Public Health 4, no. 9 (August 23, 2017): 3048. http://dx.doi.org/10.18203/2394-6040.ijcmph20173813.

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A person receiving public health services should not receive a lesser standard of service because of his/her race, gender, age, colour, national origin, disability status, occupation or any other extraneous characteristics. However, sometimes our perception based on these hidden linkages (unconscious, irrepressible, or irrational connotations) may influence our judgements resulting in unfairness which are referred as implicit biases. Such biases can result in poorer quality of care. In public health, where the ultimate motive is to ensure social justice, these implicit biases are thus quite deleterious. The purpose of this article was to examine the implicit biases in public health practice and develop recommendations for education, training and research in this discipline using the application of a novel behavioural theory, multi-theory model (MTM) of health behaviour change. A review of literature in the MEDLINE, CINAHL, Google Scholar, and ERIC databases was performed to prepare this article. The constructs of participatory dialogue in which advantages outweigh disadvantages, behavioural confidence, and changes in physical environment were discussed to initiate behaviour change devoid of implicit biases. The constructs of emotional transformation, practice for change and changes in social environment were discussed to sustain behaviour change devoid of implicit biases. Educational interventions based on MTM need to be adopted by Schools of Public Health in education of public health students and training of public health professionals. Such efforts will reduce implicit biases in the discipline of public health and improve quality of care.

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Grise, Kevin M., Lorenzo M. Polvani, and John T. Fasullo. "Reexamining the Relationship between Climate Sensitivity and the Southern Hemisphere Radiation Budget in CMIP Models." Journal of Climate 28, no. 23 (December 1, 2015): 9298–312. http://dx.doi.org/10.1175/jcli-d-15-0031.1.

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Abstract Recent efforts to narrow the spread in equilibrium climate sensitivity (ECS) across global climate models have focused on identifying observationally based constraints, which are rooted in empirical correlations between ECS and biases in the models’ present-day climate. This study reexamines one such constraint identified from CMIP3 models: the linkage between ECS and net top-of-the-atmosphere radiation biases in the Southern Hemisphere (SH). As previously documented, the intermodel spread in the ECS of CMIP3 models is linked to present-day cloud and net radiation biases over the midlatitude Southern Ocean, where higher cloud fraction in the present-day climate is associated with larger values of ECS. However, in this study, no physical explanation is found to support this relationship. Furthermore, it is shown here that this relationship disappears in CMIP5 models and is unique to a subset of CMIP models characterized by unrealistically bright present-day clouds in the SH subtropics. In view of this evidence, Southern Ocean cloud and net radiation biases appear inappropriate for providing observationally based constraints on ECS. Instead of the Southern Ocean, this study points to the stratocumulus-to-cumulus transition regions of the SH subtropical oceans as key to explaining the intermodel spread in the ECS of both CMIP3 and CMIP5 models. In these regions, ECS is linked to present-day cloud and net radiation biases with a plausible physical mechanism: models with brighter subtropical clouds in the present-day climate show greater ECS because 1) subtropical clouds dissipate with increasing CO2 concentrations in many models and 2) the dissipation of brighter clouds contributes to greater solar warming of the surface.

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40

Su, C. H., and D. Ryu. "Multi-scale analysis of bias correction of soil moisture." Hydrology and Earth System Sciences Discussions 11, no. 7 (July 29, 2014): 8995–9026. http://dx.doi.org/10.5194/hessd-11-8995-2014.

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Abstract. Remote sensing, in situ networks and models are now providing unprecedented information for environmental monitoring. To conjunctively use multi-source data nominally representing an identical variable, one must resolve biases existing between these disparate sources, and the characteristics of the biases can be non-trivial due to spatiotemporal variability of the target variable, inter-sensor differences with variable measurement supports. One such example is of soil moisture (SM) monitoring. Triple collocation (TC) based bias correction is a powerful statistical method that increasingly being used to address this issue but is only applicable to the linear regime, whereas nonlinear method of statistical moment matching is susceptible to unintended biases originating from measurement error. Since different physical processes that influence SM dynamics may be distinguishable by their characteristic spatiotemporal scales, we propose a multi-time-scale linear bias model in the framework of a wavelet-based multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to demonstrate scale-dependent biases between in situ, remotely-sensed and modelled SM, the influence of various prospective bias correction schemes on these biases, and lastly to enable multi-scale bias correction and data adaptive, nonlinear de-noising via wavelet thresholding.

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41

Yfantidou, Sofia, Pavlos Sermpezis, Athena Vakali, and Ricardo Baeza-Yates. "Uncovering Bias in Personal Informatics." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 3 (September 27, 2023): 1–30. http://dx.doi.org/10.1145/3610914.

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Personal informatics (PI) systems, powered by smartphones and wearables, enable people to lead healthier lifestyles by providing meaningful and actionable insights that break down barriers between users and their health information. Today, such systems are used by billions of users for monitoring not only physical activity and sleep but also vital signs and women's and heart health, among others. Despite their widespread usage, the processing of sensitive PI data may suffer from biases, which may entail practical and ethical implications. In this work, we present the first comprehensive empirical and analytical study of bias in PI systems, including biases in raw data and in the entire machine learning life cycle. We use the most detailed framework to date for exploring the different sources of bias and find that biases exist both in the data generation and the model learning and implementation streams. According to our results, the most affected minority groups are users with health issues, such as diabetes, joint issues, and hypertension, and female users, whose data biases are propagated or even amplified by learning models, while intersectional biases can also be observed.

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42

Cheval, Boris, Silvio Maltagliati, Layan Fessler, Ata Farajzadeh, Sarah N. Ben Abdallah, François Vogt, Margaux Dubessy, et al. "Physical effort biases the perceived pleasantness of neutral faces: A virtual reality study." Psychology of Sport and Exercise 63 (November 2022): 102287. http://dx.doi.org/10.1016/j.psychsport.2022.102287.

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43

CHEN, Hong. "Cognitive Biases among College Students with a Fat or Thin Negative Physical Self." Acta Psychologica Sinica 40, no. 7 (November 27, 2008): 809–18. http://dx.doi.org/10.3724/sp.j.1041.2008.00809.

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44

FENG, Wen-Feng, Wen-Bo LUO, Yu LIAO, Hong CHEN, and Yue-Jia LUO. "Attention Biases of Undergraduate Women with Fat Negative Physical Self: Orienting or Maintenance." Acta Psychologica Sinica 42, no. 7 (August 31, 2010): 779–90. http://dx.doi.org/10.3724/sp.j.1041.2010.00779.

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45

Towlson, Chris, Stephen Cobley, Adrian Midgley, Andrew Garrett, Guy Parkin, and Ric Lovell. "Relative Age, Maturation and Physical Biases on Position Allocation in Elite-Youth Soccer." International Journal of Sports Medicine 38, no. 03 (February 20, 2017): 201–9. http://dx.doi.org/10.1055/s-0042-119029.

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46

Spitzer, Sonja, and Daniela Weber. "Reporting biases in self-assessed physical and cognitive health status of older Europeans." PLOS ONE 14, no. 10 (October 8, 2019): e0223526. http://dx.doi.org/10.1371/journal.pone.0223526.

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47

Werle, Carolina O. C., Brian Wansink, and Collin R. Payne. "Is it fun or exercise? The framing of physical activity biases subsequent snacking." Marketing Letters 26, no. 4 (May 15, 2014): 691–702. http://dx.doi.org/10.1007/s11002-014-9301-6.

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48

Jent, Jason F., Cyd K. Eaton, Lauren Knickerbocker, Walter F. Lambert, Melissa T. Merrick, and Susan K. Dandes. "Multidisciplinary child protection decision making about physical abuse: Determining substantiation thresholds and biases." Children and Youth Services Review 33, no. 9 (September 2011): 1673–82. http://dx.doi.org/10.1016/j.childyouth.2011.04.029.

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49

Studinger, Michael, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling. "Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models." Cryosphere 18, no. 5 (May 31, 2024): 2625–52. http://dx.doi.org/10.5194/tc-18-2625-2024.

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Abstract. Differential penetration of green laser light into snow and ice has long been considered a possible cause of range and thus elevation bias in laser altimeters. Over snow, ice, and water, green photons can penetrate the surface and experience multiple scattering events in the subsurface volume before being scattered back to the surface and subsequently the instrument's detector, therefore biasing the range of the measurement. Newly formed sea ice adjacent to open-water leads provides an opportunity to identify differential penetration without the need for an absolute reference surface or dual-color lidar data. We use co-located, coincident high-resolution natural-color imagery and airborne lidar data to identify surface and ice types and evaluate elevation differences between those surfaces. The lidar data reveals that apparent elevations of thin ice and finger-rafted thin ice can be several tens of centimeters below the water surface of surrounding leads, but not over dry snow. These lower elevations coincide with broadening of the laser pulse, suggesting that subsurface volume scattering is causing the pulse broadening and elevation shift. To complement our analysis of pulse shapes and help interpret the physical mechanism behind the observed elevation biases, we match the waveform shapes with a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse, the surface roughness, and the optical scattering properties of the medium. We parameterize the scattering in our model based on the scattering length Lscat, the mean distance a photon travels between isotropic scattering events. The largest scattering lengths are found for thin ice that exhibits the largest negative elevation biases, where scattering lengths of several centimeters allow photons to build up considerable range biases over multiple scattering events, indicating that biased elevations exist in lower-level Airborne Topographic Mapper (ATM) data products. Preliminary analysis of ICESat-2 ATL10 data shows that a similar relationship between subsurface elevations (restored negative freeboard) and “pulse width” is present in ICESat-2 data over sea ice, suggesting that biased elevations caused by differential penetration likely also exist in lower-level ICESat-2 data products. The spatial correlation of observed differential penetration in ATM data with surface and ice type suggests that elevation biases could also have a seasonal component, increasing the challenge of applying a simple bias correction.

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Su, C. H., and D. Ryu. "Multi-scale analysis of bias correction of soil moisture." Hydrology and Earth System Sciences 19, no. 1 (January 6, 2015): 17–31. http://dx.doi.org/10.5194/hess-19-17-2015.

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Abstract. Remote sensing, in situ networks and models are now providing unprecedented information for environmental monitoring. To conjunctively use multi-source data nominally representing an identical variable, one must resolve biases existing between these disparate sources, and the characteristics of the biases can be non-trivial due to spatio-temporal variability of the target variable, inter-sensor differences with variable measurement supports. One such example is of soil moisture (SM) monitoring. Triple collocation (TC) based bias correction is a powerful statistical method that is increasingly being used to address this issue, but is only applicable to the linear regime, whereas the non-linear method of statistical moment matching is susceptible to unintended biases originating from measurement error. Since different physical processes that influence SM dynamics may be distinguishable by their characteristic spatio-temporal scales, we propose a multi-timescale linear bias model in the framework of a wavelet-based multi-resolution analysis (MRA). The joint MRA-TC analysis was applied to demonstrate scale-dependent biases between in situ, remotely sensed and modelled SM, the influence of various prospective bias correction schemes on these biases, and lastly to enable multi-scale bias correction and data-adaptive, non-linear de-noising via wavelet thresholding.

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