DNA Typing Results from Two Urban Subpopulations of Pakistan
Bibliographic record
Abstract
A population genetic characterization of the Araeen and Raajpoot ethnic subpopulations of Lahore City, Pakistan was undertaken in order to assess the utility of DNA typing for forensic purposes in Pakistani populations. One hundred unrelated individuals from each group were genotyped for four independently assorting loci: HLA DQAI, CSF1PO, TPOX, and TH01. Allele frequencies were calculated, one- and two-locus tests for association were conducted, and the samples were compared by contingency table tests and F-statistic estimation. Although there is expected to be some genetic divergence between the two groups, forensic needs may be satisfied with a single Pakistani database of DNA profiles. The present data suggest that nine independently assorting loci will be sufficient to provide estimated profile probabilities of the order of 10(-9) but a set of 13 loci, as employed in the U.S., would better compensate for the dependencies introduced by family membership and evolutionary history.
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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 | 0.001 | 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) | 0.002 | 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".