A Cross Sectional Descriptive Study for Estimation of Stature from Foot Length in South Indian Population
Bibliographic record
Abstract
Identification can be done by a myriad of methods and of them includes the measurement of stature by foot length The Study population includes the faculty and students of a tertiary medical care college and hospital and the residents of a district in South India between the ages group of 21-40 years. 200 members consisting of 100 male and 100 female were chosen by stratified random sampling. The height was measured by using standard height measuring instrument and foot length by a vernier calliper. A highly significant correlation was found between Stature and RFL(r=0.811) with the strength of association being more in males (r=0.677) than females (r=0.592). Ahighly significant correlation was also found between Stature and LFL (r=0.823) with the strength of association again being more in males (r=0.707) than in females (r=0.582). Between the two feet, the stature showed highly significant strong correlation with LFL (r=0.823) 2 when compared to RFL (r=0.811). By comparing the r and r values in different study groups it is seen that pooled sample shows better correlation than individual sex. Regression equations were developed for individual sex and also for the pooled data. Stature showed a highly significant positive correlation with both foot lengths with the RFL exhibiting a slightly stronger association. Regression equation for stature developed in this study with respect to the pooled data exhibited a better goodness of fit for the Left foot length
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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) | 0.002 | 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".