Assessment of patterns of temperature-dependent sex determination using maximum likelihood model selection
Bibliographic record
Abstract
Sex determination in some reptiles is independent of egg incubation temperature and is called genotypic sex determination (GSD). In many other reptiles, sexual phenotype is dependent on incubation temperature. This phenomenon is called temperature-dependent sex determination (TSD). TSD is categorized by three patterns, based on the majority sex produced at lower and higher incubation temperatures, named MF for Male-Female, FM for Female-Male, or FMF for Female-Male-Female. When large numbers of eggs are incubated at many different incubation temperatures, the assessment of TSD pattern is unambiguous, but when few eggs or few incubation temperatures are used, the categorization of TSD pattern is less straightforward. We propose a new methodology based on maximum likelihood model selection that evaluates and ranks the performance of four descriptive models of sex determination for discrete datasets. This method has the added benefit of giving standardized definitions of two commonly reported parameters of TSD: the pivotal temperature and the transitional range of temperature. Standardization of analyses will help facilitate cross-species meta-analyses of TSD in reptiles.
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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 | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".