Quantity and asymmetry of fingerprint white lines: forensic implication
Bibliographic record
Abstract
Bilateral asymmetry is one of the widely used features by proxy as an indicator of environmental and occupational stress and developmental instability. However, its application in personal identification has not been well elucidated in the literature. The present study strives to investigate the forensic implication of fingerprint white line count (FWLC) quantity and asymmetry and the potential of their utilization as complementary tools in personal identification. The objectives of the study were to determine the potential of FWLC asymmetry as a possible feature for sex and left or right of digit determination and its possible forensic implication among the Hausa population of Kano state, Nigeria. The study was a cross sectional type which comprises of 300 participants. A plain fingerprint captured using live scan techniques to determine the FWLC. Wilcoxon signed ranks and Mann-Whitney tests were used to compare the paired and independent variables. Binary logistic regression analyses were employed for determination of sex and left or right of the digit. The result shows statistically significant differences between the left and right FWLC in both sexes. FWLC exhibited leftward asymmetry in all the digits in both males and females. Significant sexual dimorphism in FWLC asymmetry was observed in all the digits except for the middle digits. Regarding the sex and left or right determination, the coefficients of discrimination of sex and left or right of digit were found to be significant for all the digits except for the middle digits for sex. The variance of sex and left or right of the digits explained by FWLC asymmetry was higher for index and ring digits. The group membership prediction was best for index and ring digits. In conclusion, the FWLC asymmetry exhibits potential in sex and left or right of the digit prediction among Hausa population. Index and ring digits were the best digits that expressed the level of dimorphism and discrimination.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".