Using a novel methodology to test whether group size affects foraging behaviour in elk
Bibliographic record
Abstract
Group formation is one of the most striking patterns in the natural world. \nElk (Cervus elaphus and C. canadensis) are well known for their social and gregarious nature, but motivations for this behaviour are not fully understood. In particular, how elk perceive and deal with predation risk and modify foraging behaviour as group size changes requires further study. \nThis thesis begins by describing how group behaviour might add to the \nsecurity of individuals, using a model that varies adult elk survival with group size. The model might explain why a Lake of the Woods, Ontario, elk population (C.canadensis manitobensisi) declined following re?introduction in a translocation program that occurred between 2000 and 2001. The population suffered initially from high levels of predation, possibly due to the predator?na?ve nature of the source population from Elk Island National Park, Alberta. A model forcing elk into one of several group sizes, each varying in degree of predation risk describes the \npredator?na?ve nature of introduced elk as contributing to the decline. If \nindividuals adapt to novel predation risks by joining larger groups with higher survival, the population stabilizes and eventually increases.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".