More haste, less speed: pilot study suggests camera trap detection zone could be more important than trigger speed to maximise species detections
Bibliographic record
Abstract
Camera traps are being used increasingly for wildlife management and research. When choosing camera models, practitioners often consider camera trigger speed to be one of the most important factors to maximise species detections. However, factors such as detection zone will also influence detection probability. As part of a rabbit eradication program, we performed a pilot study to compare rabbit (Oryctolagus cuniculus) detections using the Reconyx PC900 (faster trigger speed, narrower detection zone) and the Ltl Acorn Ltl-5310A (slower trigger speed, wider detection zone). Contrary to our predictions, the slower-trigger-speed cameras detected rabbits more than twice as often as the faster-trigger-speed cameras, suggesting that the wider detection zone more than compensated for the relatively slower trigger time. We recommend context-specific field trials to ensure cameras are appropriate for the required purpose. Missed detections could lead to incorrect inferences and potentially misdirected management actions.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".