Resource tracking by eastern chipmunks: the sampling of renewing patches
Bibliographic record
Abstract
When food patches vary in quality over time, sampling by repeated visits can allow animals to track this variation and improve their foraging success. Sampling, however, requires spending time visiting patches that are currently poor. The optimal investment in sampling should depend on characteristics of the patch, the animal, and the environment, but there are few empirical studies of these relationships in nature. Here, we describe discovery, exploitation, and sampling of randomly varying artificial food patches by free-ranging eastern chipmunks ( Tamias striatus (L., 1758)). Chipmunks effectively tracked variation over a broad time scale, discovering patches within a few days, sampling and exploiting over several weeks, and decreasing sampling when renewals ceased. Sampling allowed the chipmunks to track variation on an hourly scale through rapid discovery of renewals. Sampling rates were high (median = 0.3 visits·individual–1·h–1; range = 0–4.2). Sampling was not affected by the frequency or magnitude of patch renewal but was lower for chipmunks whose burrows were farther from the patch. Sampling is an important part of chipmunk foraging strategy, but the difficulty of estimating patch quality and renewal rate and the effects of competition may prevent a close matching between sampling rate and patch characteristics under natural conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".