An Accelerated Analytical Process for the Development of <scp>STR</scp> Profiles for Casework Samples
Bibliographic record
Abstract
Significant efforts are being devoted to the development of methods enabling rapid generation of short tandem repeat (STR) profiles in order to reduce turnaround times for the delivery of human identification results from biological evidence. Some of the proposed solutions are still costly and low throughput. This study describes the optimization of an analytical process enabling the generation of complete STR profiles (single-source or mixed profiles) for human identification in approximately 5 h. This accelerated process uses currently available reagents and standard laboratory equipment. It includes a 30-min lysis step, a 27-min DNA extraction using the Promega Maxwell(®) 16 System, DNA quantification in <1 h using the Qiagen Investigator(®) Quantiplex HYres kit, fast amplification (<26 min) of the loci included in AmpFℓSTR(®) Identifiler(®), and analysis of the profiles on the 3500-series Genetic Analyzer. This combination of fast individual steps produces high-quality profiling results and offers a cost-effective alternative approach to rapid DNA analysis.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".