Wariness of coyotes to camera traps relative to social status and territory boundaries
Bibliographic record
Abstract
The primary objective of this study was to develop a better understanding of coyote (Canis latrans) wariness particularly as it related to social status. We determined that territory status (controlling alpha, resident beta, or nonterritorial transient) affected vulnerability to photo-capture by infrared-triggered camera systems. All coyotes were wary of cameras, leading to relatively low numbers of photo-captures, most of which occurred at night. Alphas were significantly underrepresented in photographs and were never photo-captured inside their own territories. Betas were photographed inside and outside their territories, whereas transients were most often photographed on edges of territories. Both alphas and betas were photographed more often on territorial edges when outside their territories. We next addressed the question of how alphas were better able to avoid photo-capture. Alphas tracked human activity within their territories and presumably learned the locations of cameras as they were being set up. They did this either by approaching our location directly or by moving to a vantage point from where they could observe us. Betas and transients either withdrew or did not respond to human activity. Trials in which a dog was present were more likely to elicit an approach response from alphas. Avoidance of camera stations and the tracking of human activity implied wariness toward objects or locations resulting from their learned association with human presence rather than neophobia toward the objects themselves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".