Effect of Season and High Ambient Temperature on Activity Levels and Patterns of Grizzly Bears (Ursus arctos)
Bibliographic record
Abstract
Understanding factors that influence daily and annual activity patterns of a species provides insights to challenges facing individuals, particularly when climate shifts, and thus is important in conservation. Using GPS collars with dual-axis motion sensors that recorded the number of switches every 5 minutes we tested the hypotheses: 1. Grizzly bears (Ursus arctos) increase daily activity levels and active bout lengths when they forage on berries, the major high-energy food in this ecosystem, and 2. Grizzly bears become less active and more nocturnal when ambient temperature exceeds 20°C. We found support for hypothesis 1 with both male and female bears being active from 0.7 to 2.8 h longer in the berry season than in other seasons. Our prediction under hypothesis 2 was not supported. When bears foraged on berries on a dry, open mountainside, there was no relationship between daily maximum temperature (which varied from 20.4 to 40.1°C) and the total amount of time bears were active, and no difference in activity levels during day or night between warm (20.4-27.3°C) and hot (27.9-40.1°C) days. Our results highlight the strong influence that food acquisition has on activity levels and patterns of grizzly bears and is a challenge to the heat dissipation limitation theory.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".