Sex diagnosis by mesiodistal distances in permanent canines from Mexican population
Bibliographic record
Abstract
Sex diagnosis by teeth is challenging in forensic cases in Mexico; it must involve the collaboration of experts to assist in the reconstructive identification process in cases where only dental evidence is available. Through a multidisciplinary approach (odontology, anthropology, and statistics), this study aims to analyze the reliability of mesiodistal (MD) distance in vestibular view at the incisal third in permanent canines from dental models with known data to generate a specific reference in the diagnosis sex for the Mexican population. Intra and interobserver tests of the measures recorded by four observers were used. Additionally, parametric statistical methods (descriptive statistics, confidence intervals, and logistic regression models) were employed to identify the relationship between distance and sex differences. The analysis demonstrated more significant dimorphism in the left maxillary and left and right mandibular canines, and results showed a 95% confidence interval; it was determined for the MD distances. Our study generates another tool to guide how to help discrimination between females and males (biological sex) in reconstructing the Mexican population’s biological profile. This article proposes multidisciplinary forensic practice without the borders and delimited scope of each area; the identification of people in Mexico is a national problem that requires joint disciplines or team collaboration.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".