Testing ‘bigger is better’ and maternal effects hypotheses in hatchlings of the sexually dimorphic spiny softshell turtle (<i>Apalone spinifera</i>)
Bibliographic record
Abstract
Abstract The ‘bigger is better’ hypothesis (BIBH) predicts that fitness increases with body size. Eastern spiny softshell turtles (Apalone spinifera) exhibit sexual size dimorphism (SSD) at maturity, and the much larger female body size is attributed to selection favouring greater reproductive output. Hatchlings exhibit sexually dimorphic markings, but hatchling SSD has not been investigated. The BIBH predicts that hatchlings will not exhibit SSD because reproductive benefits to larger, mature females drive size dimorphism at maturity, but selection on hatchlings relates only to survival. We tested this prediction with A. spinifera hatchlings (N = 2630) incubated in controlled conditions to avoid confounding environmental effects on phenotype. Hatchling mass was similar between sexes, suggesting similar nutritional allocation to male and female offspring. Hatchling size (carapace length) was also similar between sexes. Hatchling mass and body size varied among clutches and were positively related to maternal size, suggesting morphological constraints on egg size. Surprisingly, clutch size was related only weakly to maternal size. Taken together, our results suggest that hatchling size in A. spinifera is consistent with the BIBH and that offspring quality increases with maternal age, but that maternal effects drive a large proportion of the variation in clutch size and offspring quality.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".