Caching behaviour by red squirrels may contribute to food conditioning of grizzly bears
Bibliographic record
Abstract
We describe an interspecific relationship wherein grizzly bears (Ursus arctos horribilis) appear to seek out and consume agricultural seeds concentrated in the middens of red squirrels (Tamiasciurus hudsonicus), which had collected and cached spilled grain from a railway. We studied this interaction by estimating squirrel density, midden density and contents, and bear activity along paired transects that were near (within 50 m) or far (200 m) from the railway. Relative to far ones, near transects had 2.4 times more squirrel sightings, but similar numbers of squirrel middens. Among 15 middens in which agricultural products were found, 14 were near the rail and 4 subsequently exhibited evidence of bear digging. Remote cameras confirmed the presence of squirrels on the rail and bears excavating middens. We speculate that obtaining grain from squirrel middens encourages bears to seek grain on the railway, potentially contributing to their rising risk of collisions with trains.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".