Assessing predictions from optimal egg theory for an ectotherm relative to habitat duration
Bibliographic record
Abstract
Abstract Optimal egg size theory predicts females must balance investment per offspring to maximize fitness based on environmental quality. In wetlands, environmental quality can be duration of water and predator presence. Ectotherms using habitats that dry or contain predators are likely under selection to optimize offspring production. We measured reproductive output of wood frogs (Rana sylvatica) in 30 wetlands in Subarctic Canada, where rapid climate changes are accelerating wetland drying. We predicted wetlands with short hydroperiods would have larger ova, smaller clutch sizes, and larger ovum‐to‐clutch‐sizes than wetlands with long hydroperiods or with fish predators. We found partial support for predictions with larger ova in habitats with short hydroperiods and no fish but no evidence of larger clutch sizes in wetlands with fish. Our study implicates changes to wetland hydroperiod as a source of plasticity affecting one aspect of reproductive effort (ovum size) in an ectotherm but not another (clutch size).
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".