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Record W2412125126 · doi:10.1520/jfs2002182

STR DNA Typing: Increased Sensitivity and Efficient Sample Consumption Using Reduced PCR Reaction Volumes

2003· article· en· W2412125126 on OpenAlexaff
Benoît Leclair, Joanne B. Sgueglia, Patricia C. Wojtowicz, Ann C. Juston, Chantal J. Frégeau, Ron M. Fourney

Bibliographic record

VenueJournal of Forensic Sciences · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsRoyal Canadian Mounted Police
Fundersnot available
KeywordsPolymerase chain reactionTypingDNAVolume (thermodynamics)Molecular biologySensitivity (control systems)ChromatographyBiologyDNA extractionAnalytical Chemistry (journal)ChemistryGeneticsGenePhysics

Abstract

fetched live from OpenAlex

Improvements in detection limits/sensitivity and lower sample consumption are potential benefits of reducing PCR reaction volumes used in forensic DNA typing of crime scene samples. This premise was studied first with experimental mixtures and a nine-loci megaplex, which demonstrated stochiometric amplification and accurate detection. Next, adjudicated casework samples were subjected to amplification under 15 different template DNA to PCR reaction volume ratios. Reduction of PCR reaction volume and DNA down to 10 microL and 0.500 ng, respectively, produced identical profiles with the same signal intensity and heterozygous allele peak height ratio (HR). Reduction to 5 microL and 0.063 ng yielded HR values that were slightly affected in one to three STR loci. PCR reaction volume reduction can enhance detection and sensitivity while reducing the consumption of irreplaceable crime scene samples.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.045
GPT teacher head0.319
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations60
Published2003
Admission routes1
Has abstractyes

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