Brown bear (<i>Ursus arctos</i>) foraging in a mosaic of spatially discrete and variable habitats over 25 years of shifting Pacific salmon densities
Bibliographic record
Abstract
Many foraging models assume “perfect information” and “free movement” when describing predator foraging behavior, although this is rare in nature. Here, we quantified predation by brown bears ( Ursus arctos Linnaeus, 1758) on adult sockeye salmon ( Oncorhynchus nerka (Walbaum, 1792)) in a series of spatially proximate ponds that largely satisfied both assumptions. Salmon abundance varied among years, but pond area and depth were fixed, allowing us to examine interactions between prey abundance and habitat features. We applied versions of two models to 25 years of data on the number and proportion of salmon killed by bears, modifying these models to include habitat features and temporal variability. The functional response model with a year effect fit the data well, indicating bears could take almost all salmon in ponds when salmon were scarce, but bears were sated when salmon were abundant. The proportion of salmon killed by bears was similar across habitats after correcting for pond depth and area. Overall, bears foraged across all habitats but killed higher proportions of salmon in smaller and shallower habitats, consistent with ease of capture.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".