Social information use increases with decreasing winter temperature in a passerine bird
Bibliographic record
Abstract
Foragers can gain knowledge of profitable foraging opportunities either by sampling the environment directly (asocial information) or from congeners (social information). The relative benefit of using social information over asocial information is context-specific, and social information use is expected to be particularly beneficial when the costs of acquiring asocial information are high, for example, due to high risk of starvation if asocial information fails. We investigated the plasticity of social information use in an overwintering population of black-capped chickadees ( Poecile atricapillus ) as they rediscovered an intermittently available food source. Lower temperatures impose energetic costs that increase the risk of starvation in chickadees; therefore, lower temperatures are predicted to favour higher use of social information. To test this prediction, we evaluated chickadees’ reliance on social information during foraging as ambient temperatures ranged from −11.0°C to 5.5°C. We evaluated the relative strength of reliance on social and asocial information using network-based diffusion analysis. We found increased reliance on social information transmission with decreasing temperature. Reversible plasticity of social information use may be an important mechanism to cope with low ambient temperatures, a seasonal challenge experienced by many animals.
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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".