How to feed a fish: application of giving-up density theory to aquatic systems
Bibliographic record
Abstract
Optimal foraging and patch use theory predict how an organism foraging in a heterogeneous environment should use depletable food sources. The giving-up density (GUD) experimentally quantifies a forager’s quitting harvest rate within a resource patch and predicts that a forager should have high GUDs (quit foraging sooner) when costs, such as predation risk, metabolic cost, or missed opportunity costs are high. GUDs have been widely applied in terrestrial ecology to investigate questions such as: What strategy does a forager use to find and exploit a food patch? How does perceived risk vary over space and time? How do differences in foraging efficiencies lead to coexistence? In contrast, relatively few studies in aquatic biology have employed GUDs to measure the patch use foraging behavior of fishes. Here, we present the practical application of the GUD as a metric of patch use and its distinguishing features as a metric of foraging behavior for fishes. Specifically, we discuss the conceptual basis of the GUD, how GUDs are measured, and experimental application of GUDs. We then present how GUDs can complement studies of fish foraging and the potential for further use in studies of fish behavior.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".