Utility of the bucket cable trap to capture American black bears
Bibliographic record
Abstract
ABSTRACT Most American black bear ( Ursus americanus ) population studies involving live capture have used foot‐hold restraints or barrel and culvert traps, but new capture methods, including the bucket cable trap, are increasingly being used by wildlife management agencies and researchers. Although the bucket cable trap has been used to capture black bears and grizzly bears ( U. arctos ) in the United States and Canada, quantitative assessments of its capture efficiency, injury rates, and capture biases are lacking. We addressed this gap in knowledge using a camera‐trap‐based study of bucket‐cable‐trap capture methodology. Between 12 May and 12 August 2015, we placed remotely triggered cameras at active bucket‐trap sites throughout southeastern Oklahoma, USA. During 1,285 camera‐trap‐nights, we recorded 711 black bear visitation events and 106 successful captures. Of the 402 visitation events in which the trap was active, 26.3% resulted in a successful capture. Incidental captures were limited to northern raccoons ( Procyon lotor ). Sex, previous capture, and mass characteristics appeared to affect the capture process, indicating that it is important to keep capture heterogeneity in mind when characterizing population demographics and calculating abundance using this capture method. © 2018 The Wildlife Society.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".