Does temporal variation in predation risk affect antipredator responses of larval Indian Skipper Frogs (<i>Euphlyctis cyanophlyctis</i>)?
Bibliographic record
Abstract
Predation risk varies on a moment-to-moment basis, through day and night, lunar and seasonal cycles, and over evolutionary time. Hence, it is adaptive for prey animals to exhibit environment-specific behaviour, morphology, and (or) life-history traits. Herein, the effects of temporally varying predation risk on growth, behaviour, morphology, and life-history traits of larval Indian Skipper Frogs (Euphlyctis cyanophlyctis (Schneider, 1799)) were studied by exposing them to no risk, continuous, predictable, and unpredictable risks at different time points. Our results show that larval E. cyanophlyctis could learn the temporal pattern of risk leading to weaker behavioural responses under predictable risk and stronger responses to unpredictable risk. Temporally varying predation risk had a significant impact on tadpole morphology. Tadpoles facing continuous risk had narrow tail muscles. Tadpoles facing predictable risk during the day were heavy with wide and deep tail muscles, whereas those facing predictable risk at night had long tails. Tadpoles facing unpredictable risk were heavy with narrow tail muscles. Metamorphic traits of E. cyanophlyctis were also affected by the temporal variation in predation risk. Tadpoles facing predictable risk during the day emerged at the largest size. However, tadpoles facing predictable risk at night and unpredictable risk metamorphosed earlier, whereas those facing continuous risk metamorphosed later.
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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) | 0.001 | 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".