On the need for rigorous welfare and methodological reporting for the live capture of large carnivores: A response to de Araujo et al. (2021)
Bibliographic record
Abstract
Abstract De Araujo et al. ( Methods in Ecology and Evolution , 2021, https://doi.org/10.1111/2041‐210X.13516 ) described the development and application of a wire foot snare trap for the capture of jaguars Panthera onca and cougars Puma concolor . Snares are a commonly used and effective means of studying large carnivores. However, the article presented insufficient information to replicate the work and inadequate consideration and description of animal welfare considerations, thereby risking the perpetuation of poor standards of reporting. Appropriate animal welfare assessments are essential in studies that collect data from animals, especially those that use invasive techniques, and are key in assisting researchers to choose the most appropriate capture method. It is critical that authors detail all possible associated harms and benefits to support thorough review, including equipment composition, intervention processes, general body assessments, injuries (i.e. cause, type, severity) and post‐release behaviour. We offer a detailed discussion of these shortcomings. We also discuss broader but highly relevant issues, including the capture of non‐target animals and the omission of key methodological details. The level of detail provided by authors should allow the method to be properly assessed and replicated, including those that improve trap selectivity and minimize or eliminate the capture of non‐target animals. Finally, we discuss the central role that journals must play in ensuring that published research conforms to ethical, animal welfare and reporting standards. Scientific studies are subject to ever‐increasing scrutiny by peers and the public, making it more important than ever that standards are upheld and reviewed. We conclude that the proposal of a new or refined method must be supported by substantial contextual discussion, a robust rationale and analyses and comprehensive documentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.315 | 0.538 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.045 | 0.047 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".