The influence of biological affinity and sex on morphometric parameters of the clavicle in a South African sample
Bibliographic record
Abstract
OBJECTIVES: The present study aims to investigate the influence of environmental and functional factors associated with biological affinity and musculature on sexual dimorphism of the clavicle, and to develop population-specific methods of sex estimation from the clavicle. METHODS: The maximum length of the clavicle (MAXL), the sagittal diameter of the clavicle (SAGD), and the vertical diameter of the clavicle (VERD) were measured. The left clavicles of 198 South African coloured individuals (108 males and 90 females) were examined. RESULTS: Overall, the results of this study indicate that the SAGD of the clavicle is more sexually dimorphic than the VERD in the South African coloured sample. When the black American, white American, and Greek discriminant functions were applied to the South African coloured metric data, females were more accurately classified than males overall. Population-specific discriminant functions were created for the South African coloured sample. The original accuracy showed that females (85.5%) were more accurately classified than males (78.1%). Overall, the multivariate discriminant function demonstrated a higher correct classification of South African coloureds than the univariate discriminant functions. The results also suggest that univariate discriminant function equations are more accurate for sex estimation than univariate sectioning points in the South African coloured sample. CONCLUSIONS: Overall, the results of the present study indicate that the clavicle is an accurate predictor of sex and its dimensions are population-specific. Therefore, discriminant functions of the clavicle should only be used for sex estimation in forensic anthropology with the populations from which they were derived. © 2018 Wiley Periodicals, Inc.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".