Time to feed: How diet, competition, and experience may influence feeding behaviour and cannibalism in wood frog tadpoles Lithobates sylvaticus
Bibliographic record
Abstract
Abstract Wood frog Lithobates sylvaticus tadpoles develop in temporary wetlands where high population densities can force tadpoles into aggregations that intensify competition and can lead to cannibalism. However, chemical alarm cues released from injured conspecifics could also dissuade cannibalism. The purpose of this study was to test mechanisms that may influence cannibalistic behaviour. We tested whether the tendency of tadpoles to consume conspecifics would increase with the presence of competition and/or cues of profitable diets. Tadpoles placed in 1L experimental containers were tested for feeding initiation times of multiple diets, including conspecific tissues and conspecific tissues combined with chemical cues from the alternative diets (brine shrimp and cornmeal). Tadpoles were tested in the presence and absence of a competitor, and at multiple times over the course of the study. Tadpoles exhibited an altered response to diets over time; however the presence of a competitor reduced response times to all diets including conspecific tissues. Similarly, the presence of specific diets also reduced the response time of tadpoles to conspecific tissues. These results suggest competition among feeding tadpoles could result in aggressive behaviour leading to indiscriminate predation and cannibalism.
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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 | 0.000 |
| 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) | 0.002 | 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".