Recognizing sexual dimorphism in the fossil record: lessons from nonavian dinosaurs
Bibliographic record
Abstract
Abstract The demonstration of sexual dimorphism in the fossil record can provide vital information about the role that sexual selection has played in the evolution of life. However, statistically robust inferences of sexual dimorphism in fossil organisms are exceedingly difficult to establish, owing to issues of sample size, experimental control, and methodology. This is particularly so in the case of dinosaurs, for which sexual dimorphism has been posited in many species, yet quantifiable data are often lacking. This study presents the first statistical investigation of sexual dimorphism across Dinosauria. It revisits prior analyses that purport to find quantitative evidence for sexual dimorphism in nine dinosaur species. After the available morphological data were subjected to a suite of statistical tests (normality and unimodality tests and mixture modeling), no evidence for sexual dimorphism was found in any of the examined taxa, contrary to conventional wisdom. This is not to say that dinosaurs were not sexually dimorphic (phylogenetic inference suggests they may well have been), only that the available evidence precludes its detection. A priori knowledge of the sexes would greatly facilitate the assessment of sexual dimorphism in the fossil record, and it is suggested that unambiguous indicators of sex (e.g., presence of eggs, embryos, medullary bone) be used to this end.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".