Proximate mechanisms that contribute to female-biased sexual size dimorphism in an anguid lizard
Bibliographic record
Abstract
Various proximate mechanisms have been proposed to explain sexual size dimorphism (SSD) in vertebrates. Identifying the proximate causation of SSD allows insight into the ultimate reasons why SSD exists. I explored whether differential growth rates and (or) mortality explain SSD in the lizard Elgaria coerulea (Baird and Girard, 1852). I estimated growth parameters for males and females using the logistic-by-weight growth curves and determined survivorship using two complimentary methods: standard life-table calculations and capturerecapture methods. The former calculated age-specific survivorship, whereas the latter tested for differences in survivorship between males and females while considering differences in their recapture rates. I considered age-specific SSD as further evidence of SSD independent of differential mortality. Differences in growth asymptote, not intrinsic growth rate, contribute to SSD in this population. SSD is not due to differential mortality, as there is no difference in survivorship of males and females over 3 years of age. In addition, there is age-specific SSD with females larger than males for individuals 4 years of age and greater. The female-biased SSD may be a result of selection for large body size, although further studies are necessary to identify the ultimate cause of SSD in this species.
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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 | 0.001 | 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".