The effects of constant and fluctuating incubation temperatures on sex determination, growth, and performance in the tortoise<i>Gopherus polyphemus</i>
Bibliographic record
Abstract
Temperature-dependent sex determination is one of the best documented yet evolutionarily enigmatic sex-determining systems. The classical theoretical framework suggests that temperature-dependent sex determination will be adaptive when males and females benefit differentially from development at certain temperatures. Empirical evidence has not provided convincing support for this "differential-fitness" hypothesis. Furthermore, since most experiments utilize constant temperature incubation treatments to explore phenotypic response to temperature, few studies have addressed the consequences of incubation under natural conditions. In this study I utilized constant-temperature laboratory incubations and natural-nest incubations to determine the effects of temperature on sex, size, growth, and locomotor performance in the tortoise Gopherus polyphemus. Constant-temperature incubations do induce substantial growth and performance variation in these tortoises. However, the data do not clearly support the differential-fitness hypothesis because (i) growth variation does not result in adult size dimorphism, (ii) performance differences are confined to a very short period after hatching, and (iii) natural incubation temperatures do not vary sufficiently to produce significant phenotypic variation in traits other than sex.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".