Incorporating geographic context into coyote and wolf livestock depredation research
Bibliographic record
Abstract
Abstract Applying research results to new locations and situations can be confounded by differences in the geographic context between the original and the applied study sites. Replication studies and meta‐analyses may be similarly hindered. We investigated how often canid management research reports (e.g., journal articles, conference proceedings) included information on historical/current lethal control, alternative prey availability, landscape features, and seasonal and settlement characteristics. We included experimental research published between 1970 and 2018, focusing on livestock depredations by wolves and coyotes in North America. Reporting on contextual factors was highly variable; seasonal context was included in 83% of research findings; human settlement characteristics were reported in only 8%. Contextual information was more common in journal versus grey literature, and in reports with academic versus government‐affiliated primary authors. Discussions of the effects of contextual factors on livestock depredation mitigation strategies were underdeveloped. Yet, geographic context of research is vital; it can alter animal behaviour and reduce the efficacy of applied mitigation. We suggest reporting guidelines to improve comparisons and meta‐analysis opportunities, which may enhance comparisons and management decision making.
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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 | 0.051 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".