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Record W2037560118 · doi:10.1002/elps.200305718

Precision and accuracy in fluorescent short tandem repeat DNA typing: Assessment of benefits imparted by the use of allelic ladders with the AmpF<i>l</i>STR®Profiler Plus™ kit

2004· article· en· W2037560118 on OpenAlexaff
Benoît Leclair, Chantal J. Frégeau, Kathy L. Bowen, Ron M. Fourney

Bibliographic record

VenueElectrophoresis · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsRoyal Canadian Mounted Police
Fundersnot available
KeywordsGenotypingMicrosatelliteTypingAlleleBiologyGeneticsPopulationGel electrophoresisMolecular biologyGenotypeGeneMedicine

Abstract

fetched live from OpenAlex

Base-calling precision of short tandem repeat (STR) allelic bands on dynamic slab-gel electrophoresis systems was evaluated. Data was collected from over 6000 population database allele peaks generated from 468 population database samples amplified with the AmpF/STR Profiler Plus (PP) kit and electrophoresed on ABD 377 DNA sequencers. Precision was measured by way of standard deviations and was shown to be essentially the same, whether using fixed or floating bin genotyping. However, the allelic ladders have proven more sensitive to electrophoretic variations than database samples, which have caused some floating bins of D18S51 to shift on occasion. This observation prompted the investigation of polyacrylamide gel formulations in order to stabilize allelic ladder migration. The results demonstrate that, although alleles comprised in allelic ladders and questioned samples run on the same gel should migrate in an identical manner, this premise needs to be verified for any given electrophoresis platform and gel formulation. We show that the compilation of base-calling data is a very informative and useful tool for assessing the performance stability of dynamic gel electrophoresis systems, stability on which depends genotyping result quality.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.271
Teacher spread0.254 · 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 teacher head, 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

Citations11
Published2004
Admission routes1
Has abstractyes

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