Density‐dependent foraging effort of Deer Mice (<i>Peromyscus maniculatus</i>)
Bibliographic record
Abstract
Summary Little is known about how population density affects the foraging behaviour of individuals. Simple models are developed to predict the net effect of density on the quitting‐harvest rates of optimal foragers. The theory was tested with experiments that measured the foraging behaviour of free‐ranging Deer Mice under control and reduced densities. An increased density of conspecifics may (a) reduce the costs of foraging by increasing competition for resources (reduces the energetic state of the forager; competition hypothesis) or (b) increase the costs of foraging by increasing the value of time spent on social activities (social benefits hypothesis). A reduction in the costs of foraging caused by competition will reduce the quitting‐harvest rate of an optimal forager, whereas an increase in the value of alternative activities will increase the quitting‐harvest rate. Both hypotheses predict a reduction in optimal foraging time with increased density. The hypothesis that applies to Deer Mice ( Peromyscus maniculatus , Wagner) was assessed by measuring their foraging activity and quitting‐harvest rates at control and reduced population densities on four study plots located in boreal forest in north‐western Ontario, Canada. Deer Mice increased their per capita foraging activity and their quitting‐harvest rates when population densities were reduced. The results confirm the very important role of competition in the behaviour of optimal foragers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".