Mitochondrial DNA Validation in a State Laboratory*<sup>,†</sup>
Bibliographic record
Abstract
Because of the inception of the FBI Regional mitochondrial DNA (mtDNA) laboratories, many do not see establishing state/local mtDNA processing laboratories as a priority. Yet there is a long-term need for mtDNA processing that will exceed the capabilities of the FBI Regional mtDNA laboratories and the few other laboratories that are currently processing mtDNA, and that need can be fulfilled by state/local laboratories. Thus, the DNA Unit of the Delaware Office of the Chief Medical Examiner (OCME-DNA Unit) completed validation of in-house mtDNA testing in January 2007. The validation plan for mtDNA processing included the following sections: preliminary research, sensitivity and contamination studies, ExoSAP-IT optimization, BigDye optimization, sequencing and 310 optimization, sample preparation and extraction optimization, heteroplasmy, mixtures, and reproducibility. All sections of the validation were successfully completed, and mtDNA processing of skeletal remains, teeth, and hairs, as well as blood and buccal reference samples was adopted by the OCME-DNA Unit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".