Bibliographic record
Abstract
Abstract Foraging theory can be used to explain two important phenomena, how and why organisms choose particular foodstuffs (Optimal Diet Theory) and how long individuals will remain in resource patches before seeking others (marginal value theorem). The theories are analytical and produce explicit, quantitative predictions. In order to test predictions from foraging theory, one must tweak the theory to account for details of an organism's natural history. In general, experimental tests of the theory support its predictions but most often in a qualitative, not quantitative manner. Foraging theory can be applied to a wide range of resources beyond the classic food examples including mates and hosts for parasitoids. The conditions under which a game theoretical approach is best used to explain foraging decisions are, for example when the performance of a focal individual depends on the number and behaviour of other competitors in any given patch. Key Concepts Resources (e.g. food) can vary in quality as well as in distribution (e.g. aggregated). Optimal foraging theory was developed to explain how and why foods of particular quality are chosen by resource‐seeking individuals. Further, because resources are often patchily distributed, foragers must decide when to give up and seek other patches. Such decisions can be understood by employing the marginal value theorem, a theorem that falls under the foraging theory umbrella. Foraging theory models are quantitative and explicit. For example, they predict diet breadth (i.e. which resources should be included in the diet of a foraging individual) under a specific set of environmental conditions. Foraging theory predictions can be tested using wild or domesticated organisms in the laboratory or in nature. Tests of foraging theory predictions generally support the theory in a qualitative sense (e.g. the diet will expand as highest‐quality resources become rare) but often not quantitatively (e.g. the exact resource value at which diet expansion will occur). Classic foraging theory ignores the physiological state of foraging organisms (e.g. energy reserves) but the theory can easily be modified to take into account such parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.098 | 0.042 |
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".