Demographical and morphological differences among coyotes (<i>Canis latrans</i>) relative to sampling method
Bibliographic record
Abstract
Collection methods can be biased, leading to misperceptions of population composition. We tested if collection method (footholds, snares, and shooting) gave different perceptions of demography or morphology of 3539 eastern coyotes (Canis latrans Say, 1823). We found no differences in sex ratios of animals among methods, but did find some evidence that younger, lighter, and smaller animals were more likely to be collected by footholds than with snares. Female reproductive histories (placental scars) did not differ among methods. In a subsample of 232 animals, we found no evidence of differences in helminth parasitism relative to collection method. Overall, our large sample for the non-parasite analyses facilitated finding statistical significance; the biological implications hinge on the precision required in estimating population composition and the focal characteristics being compared. For example, mass was 5.3% lower for coyotes caught with footholds versus snares and 10.4% lower for coyotes caught with footholds versus being shot, whereas linear trait measurements of coyotes caught with footholds were generally smaller by at most 4.5% compared with other methods (broadly consistent with linear versus volumetric measurements). Our study provides important baseline information for making inferences about populations of coyotes (and other species) sampled using only a single collection method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".