Latitudinal variation in cranial dimorphism in <i>Macaca fascicularis</i>
Bibliographic record
Abstract
This study examines latitudinal and insular variation in the expression of sexual dimorphism in cranial length in three geographical groupings of Macaca fascicularis. In addition, the relationship between cranial length dimorphism (CLD) and sex-specific size is examined. The results of the study identified a significant relationship between CLD and latitude for only one of the three geographic groupings. Sex-specific relationships between cranial length and CLD were detected. The pattern of these relationships varied by geographic grouping. This study is important because it demonstrates that despite very similar levels of CLD in a single primate species, there exists important geographic variability in the correlates of that dimorphism. I suggest that geographically varying ecological factors may influence sex-specific natural selection and the intensity of CLD in M. fascicularis. Gaining a better understanding of this geographical variability will require that future research examines morphological variation, including CLD, within its corresponding ecological and social contexts. Such research should be comparative, and incorporate multiple geographically separated populations with disparate environmental settings.
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 | 0.000 | 0.000 |
| 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".