Follow the leader: social cues help guide landscape-level movements of American black bears (<i>Ursus americanus</i>)
Bibliographic record
Abstract
Solitary, facultative migrating animals must make decisions each year on whether, when, and where to migrate. Factors influencing individuals in their movement choices are poorly understood. American black bears (Ursus americanus Pallas, 1780) commonly migrate in late summer to areas of concentrated foods before winter denning; some bears also move long distances to dens. We radio-tracked seasonal migrations of >200 bears in Minnesota, USA, over 10 years. We observed concurrences in movements that suggested social coordination among individuals, including (i) individuals with neighboring summer ranges traveling to the same distant feeding and (or) denning areas, (ii) shared travel routes with use staggered through time, and (iii) instances of ≥2 individuals traveling in loose tandem over tens of kilometres. We sought to explain the mechanism for these coordinated migrations by comparing our observations to the predictions of six hypotheses: instinct, landscape morphology, habitat gradients, long-distance olfaction, maternal teaching, and conspecific cueing. The most parsimonious explanation was that bears follow other bears, with social cueing likely mediated through chemical communication. Males likely play a key role in social transmission of knowledge of the nutritional landscape via a system of travel routes and information centers that benefits the entire population.
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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 | 0.001 | 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) | 0.001 | 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".