The economics of resource tracking in a solitary forager, the eastern chipmunk (Tamias striatus) /
Bibliographic record
Abstract
In a variable environment, the ability to track food resources that vary in time and space may increase the foraging efficiency of individuals. Tracking can be accomplished by repeatedly visiting patches, but this sampling will be economical only if its benefits outweigh its costs. I examined the effects of patch characteristics and social factors on sampling using simulation models and both large- and small-scale field experiments on eastern chipmunks ( Tamias striatus). In the first experiment, chipmunks discovered large renewing patches within a few days, sampled them frequently enough to detect most renewals, and then decreased their sampling effort after renewal ceased, showing that they can track patches over both long and short time scales. Sampling rate was higher for animals that lived near the patch, for animals that were more aggressive while in the patch, and when the number of other animals that sampled was high, but was unaffected by the quantity and frequency of renewal. I developed a model, which predicts that the optimal sampling frequency should increase with the frequency and duration of renewal and with the rate of gain in the patch, and decrease with the duration of each sampling trip. An extension of this model predicts that conspecifics will affect even non-group foragers, by competing for food and providing social information. A second field experiment showed that chipmunks decreased their sampling in response to higher competition. Although chipmunks used social information to discover a patch, there was no indication that social information caused a decrease in sampling. In conclusion, sampling to keep track of varying patches is an important component of the foraging behaviour of chipmunks. Optimal sampling behaviour is affected by patch characteristics and sampling rate will depend on (i) the ease with which animals can estimate these characteristics, (ii) the level of competition, which can alter the patch valu
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".