Assessment of patterns of temperature-dependent sex determination using maximum likelihood model selection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it