Does coyote diet vary seasonally between a protected and an unprotected forest landscape?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In forested areas of the northern portion of their range, coyote (Canis latrans) populations are thought to depend mainly on areas disturbed by humans. Within a forested landscape, we analyzed scat contents to study seasonal variations in coyote diet, from January to December 1996, between a protected area (Kouchibouguac National Park, New Brunswick, N = 311) and an adjacent unprotected area (N = 364). Coyote diet changed significantly between May-July and August-September in both areas, and between October-December and January-April in the protected area. From January to July, the proportion of snowshoe hare (Lepus americanus) in coyote diet was significantly higher in the unprotected area than in the protected area, but no other items differed between areas. Diet also differed between the two areas during August-December. In the protected area, the proportion of mammals in the diet was significantly lower, while the proportions of fruits and insects were significantly higher. Diet diversity was maximum during August-September in both areas. During January-April, diet diversity was higher in the protected area. Our results suggest that during winter, human-induced habitat alterations increase snowshoe hare vulnerability to coyotes and thus favour coyote populations. However, during summer, human persecution seems to reduce the daylight activity of coyotes and limits their use of open habitats, thereby limiting their consumption of fruits and insects. We suggest that the level and type of human disturbance could have important implications for coyote foraging behaviour and might be a confounding factor for temporal or spatial comparisons of coyote diet.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it