Big and bad: how relative predator size and dietary information influence rusty crayfish (<i>Faxonius rusticus</i>) behavior and resource-use decisions
Bibliographic record
Abstract
Prey animals use the information that they extract from predator cues to assess risk. Animals can obtain information about the relative size of predators and their dietary constituents from odor cues that predators deposit in the environment. However, it is currently unknown how prey animals respond when presented with two or more pieces of information about a predator. Rusty crayfish (Faxonius rusticus (Girard, 1852)) were exposed to odors from predatory largemouth bass (Micropterus salmoides (Lacepède, 1802)) that were fed four different diets and also varied in size relative to the crayfish subjects. A series of analyses of covariance (ANCOVA) indicated that rusty crayfish altered their macrophyte consumption, foraging behavior, and shelter-use behavior depending on the relative size and dietary information presented by the largemouth bass. This study demonstrates that prey consider and respond to multiple aspects of a predatory threat when making resource-use decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".