Genetic study with autosomal STR markers in people of the Peruvian jungle for human identification purposes
Bibliographic record
Abstract
In the present study allele frequencies and other population parameters were determined for forensically-relevant Short Tandem Repeat (STR) markers in 278 Peruvians born and resident in the Peruvian jungle in total and separated into the subpopulations Amazonas, Loreto, and Madre de Dios. The samples were analyzed using the VeriFilerTM Express kit which enables typing of 23 STR loci and an amelogenin marker for sex determination. The parameters assessed and reported herein were allele frequencies, the power of discrimination (PD), departures from Hardy-Weinberg equilibrium, and estimates of the population distances. Under the assumption of independence, when comparing the population of the Peruvian jungle with a Peruvian mestizo population, Aymara population of Peru, Ashaninca of Peru, Hispanic Americans, and a Bolivia mestizo population, the largest genetic distance (Fst) was with the Hispanic population (Fst = 0.0343), and the smallest was with the Peruvian mestizo population (Fst = 0.0126). The jungle subpopulation showed greater distances in some comparisons. This study provides population data from a unique population residing in the Peruvian jungle which could be used to estimate various statistics of forensic interest for human identification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".