Stature estimation in male and female populations of India and Nigeria depending on other anthropometric parameters using multiple regression analysis
Bibliographic record
Abstract
In cases of mass disasters, accidents, or criminal investigations where the identity of victims is unknown certain basic anthropological parameters are helpful in ascertaining these like race, sex, age and stature. Estimating stature using multiple body measurements such as shoulder breadth, foot length, thigh length, and knee height is a common approach in anthropometry and forensic anthropology. The presence of sex and population differences in anthropometric indicators allows these measurements to be used not only to estimate the stature of an individual, but also to determine sex, different races or populations based on skeletal remains. The purpose of the study is to develop and practically verify the work of regression equations for estimating stature depending on other anthropometric indicators of men and women of two ethnically diverse populations. For this study, anthropometric data were gathered from two distinct population groups: Indian (n=102) and Nigerian (n=205). Basic demographic details along with measurements of shoulder breadth, sitting shoulder height, sitting foot length, sitting knee height, and sitting thigh length were obtained using standardized techniques as per the established anthropometric protocols. Statistical analysis was performed using appropriate software packages such as SPSS, R, or SAS. The multiple regression method was used to estimate body length depending on other anthropometric indicators. As a result of the conducted multiple linear regression analysis, reliable relationships between stature and specific anthropometric measurements in Nigerian and Indian men and women were established. It was found that stature is highly likely to depend on knee height in a sitting position in Nigerian women (R2=0.531, p<0.001), as well as hip length, foot length, and shoulder height in a sitting position in Indian men (R2=0.725, p <0.001). Stature in Indian women reliably depends on hip length and foot length in a sitting position, and in Nigerian men - on hip length, foot length, shoulder width and shoulder height in a sitting position, but the regression equations have a coefficient of determination less than 0.5 (respectively, R2=0.463, p<0.001 and R2=0.405, p<0.001) and therefore do not have much significance for forensic purposes. Additional groups (30 people for each category) were used to test the obtained regression equations. The high correlation coefficients (0.6
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".