Estimation Of Height / Stature From Facial Parameters In Indore Population
Bibliographic record
Abstract
All face parameter data was transformed from millimeters to centimeters. Data was analysed using SPSS (Statistical Package for the Social Sciences) on Windows XP Professional. Various statistical tests were performed on the obtained data, including mean, standard deviation, regression analysis, standard error of estimate, and Karl Pearson's correlation coefficient. We ran statistical analysis on the data to find a correlation between various face attributes and height, and we compared the findings for men and women. The Indore population's gender difference marking points were determined using the formula Mean ± 3SD. These points will be valuable xiii for future usage in medicolegal instances for determining the sex of an unknown sample. The examination of stature was done using regression models. Using them on a different sample of 25 boys and 25 females from Indore allowed us to assess their dependability as well. Results for the Indore, Hindu, Muslim, and Christian populations may be found in the regression equations supplied by this research, which assesses stature from face parameters. When forensic examinations include solely face remains, these methods have been shown accurate and reliable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".