Effects of diet quality and stress on interference behaviour of larval ringed salamanders
Bibliographic record
Abstract
Interference behaviour (aggression and cannibalism) can be influenced by both intrinsic factors, such as animal physiology, size, or motivation, and extrinsic factors, such as presence of competitors, predators, or prey. Our experiment examined the effects of differences in diet quality of focal salamanders and their opponents, and levels of handling on biting by pairs of larval ringed salamanders, Ambystoma annulatum , before and after presentation of prey. Diet quality and handling affect the intrinsic qualities of body size/condition and stress, respectively. Presence of prey and diet quality of opponents are extrinsic factors. Unstressed larvae on high-quality diets bit their high-condition opponents more frequently than those that had been on low-quality diets in both the presence and absence of food. Stressed larvae (all on high quality diets) showed low levels of biting when food was absent. However, when food was present, the level of biting depended on the diet quality of their opponents: stressed larvae bit opponents on low-quality diets more than opponents on high-quality diets. Overall, both intrinsic and extrinsic factors influenced interference behaviour, with larvae on high-quality diets exhibiting relatively high levels of biting unless they had experienced handling stress, and the effect of handling stress depending on the condition of the opponent in paired interactions.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".