Does an athlete's anger differ by sport type and gender?

Anger is an emotion that is frequently associated with a bad reputation. Anger has proven to play an effective role in certain athletic achievements; however, it is unknown which sport and gender have the athletes whose performance is most influenced by anger. In this study, we administered the STAXI-2 to determine relationships between gender and levels of athlete anger in 156 British athletes across a range of contact and non-contact sports and competitive levels (from professional/Olympians to recreational). We investigated differences in levels of anger in relation to the sport they played. Although not statistically significant, the results indicated that male athletes scored higher in trait, expression-out, anger control-out, and overall anger index, but females scored higher in state anger. The findings revealed that athletes in contact sports have higher levels of trait anger, but non contact athletes have higher levels of state anger. This study’s findings imply that anger does not influence all athletes similarly because anger is subjective to persons and sports

Contingency Management: Dealing Abstinence from Methamphetamines

Presented at the 2022 Virtual Northwest Medical Research Symposiu

Ambiguity in guideline definitions introduces assessor bias and influences consistency in IUCN Red List status assessments

The IUCN Red List is the most widely used tool to measure extinction risk and report biodiversity trends. Accurate and standardized conservation status assessments for the IUCN Red List are limited by a lack of adequate information; and need consistent and unbiased interpretation of that information. Variable interpretation stems from a lack of quantified thresholds in certain areas of the Red List guidelines. Thus, even in situations with sufficient information to make a Red List assessment, inconsistency can occur when experts, especially from different regions, interpret the guidelines differently, thereby undermining the goals and credibility of the process. Assessors make assumptions depending on their level of Red List experience (subconscious bias) and their personal values or agendas (conscious bias). We highlight two major issues where such bias influences assessments: relating to fenced subpopulations that require intensive management; and defining benchmark geographic distributions and thus the inclusion/exclusion of introduced subpopulations. We suggest assessor bias can be reduced by refining the Red List guidelines to include quantified thresholds for when to include fenced/intensively managed subpopulations or subpopulations outside the benchmark distribution; publishing case studies of difficult assessments to enhance cohesion between Specialist Groups; developing an online accreditation course on applying Red List criteria as a prerequisite for assessors; and ensuring that assessments of species subject to trade and utilization are represented by all dissenting views (for example, both utilitarian and preservationist) and reviewed by relevant Specialist Groups. We believe these interventions would ensure consistent, reliable assessments of threatened species between regions and across assessors with divergent views, and will thus improve comparisons between taxa and counteract the use of Red List assessments as a tool to leverage applied agendas.University of Bangor, University of Pretoria, CIB, the Scientific Authority of the South African National Biodiversity Institute

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey\u27s 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network. We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score \u3c 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret