Time-concentrated sampling: a simple strategy for information gain at a novel, depleted patch
Bibliographic record
Abstract
When an animal has found and consumed food at a new location, information about whether and when food will be present again could improve future foraging efficiency. A series of rapid returns followed by less frequent visits and finally abandonment of the patch could provide such information. By analogy with area-concentrated (area-restricted) search, we call this hypothesized pattern “time-concentrated sampling”. We tested whether eastern chipmunks ( Tamias striatus (L., 1758)) would show time-concentrated sampling in the field and whether the pattern of visits would be affected by patch value. We used peanuts to induce animals to discover a small patch of sunflower seeds. After depleting the patch, returning to find it empty, and leaving without food, 36 of 40 animals returned on sampling visits. Sampling rate was initially high and declined over 4 h. The number of peanuts and number of visits where seeds were obtained positively predicted sampling rate, but the volume of sunflower seeds presented and the distance to the burrow did not. We conclude that chipmunks exhibit flexible time-concentrated sampling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".