Bibliographic record
Abstract
An optimal habitat-selecting organism should use a dispersal strategy that enables occupation of the habitat yielding greatest fitness. The strategy is complicated when habitat quality varies through time. Theory predicts that the long-term distribution of individuals will match mean habitat quality while undermatching current habitat quality. I tested the prediction with experiments on controlled populations of meadow voles occupying two pairs of field enclosures. I released equal numbers, and equal sexes, of voles in each enclosure, and varied resource abundance between enclosures by supplemental feeding. I measured the voles' response with giving-up densities (GUDs) in artificial foraging patches, and with live-trapping at the end of the experiment. The data were consistent with only one of four a priori dispersal models. Giving-up densities declined with resource supply because short-term supply had no effect on population density. GUDs were invariant to the time course of the experiment because densities were proportional to each enclosure's long-term mean quality. Similar patterns in sex ratios and patterns of habitat occupation by juvenile voles born during the experiment reinforce the interpretation of time-averaged habitat matching. This study adds to the cumulating evidence that strategies of space use converge toward behavioral and evolutionary optima.
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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 | 0.001 | 0.003 |
| 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".