Forensic approach: Analysis of gender based variation in palm prints within Pakistani population
Bibliographic record
Abstract
Background: In the modern era of forensic science, palm prints are commonly used for criminal investigations and commercial purposes. For instance, prints from the base of the palm (or palm heels) are often found at crime scenes when a criminal removes their gloves during the commission of a crime, leaving their palm prints exposed to the environment. This research poses challenges for anthropologists and forensic experts. The goal of the current study is to examine the effect of gender factors on palm print patterns. Discriminatory features of palm prints were analyzed for their potential legal or commercial applications. Results: All palm prints were analyzed based on the following parameters: ridge density, T.DOT value, types of creases, and anthropometry. Using Statistix 8.1 software, the p-values and chi-square values of all palm prints were calculated and presented graphically for both hands. In this study, our results revealed that ridge density is higher in females than in males. Significant differences were observed in stature and hand measurements (length, width, strength) between the two genders, with males exhibiting significantly higher values than females. Conclusion: Various parameters such as ridge density, anthropometry, crease patterns, and the total degree of transversality determine the physical identity of individuals based on gender. A conclusive method for identifying gender from latent palm prints has been established, which will assist forensic experts and investigators in apprehending suspects.
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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 | 0.001 | 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) | 0.004 | 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".