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Record W2920546078 · doi:10.1016/j.fsigen.2019.03.003

Applicability of the SNPforID 52-plex panel for human identification and ancestry evaluation in a Brazilian population sample by next-generation sequencing

2019· article· en· W2920546078 on OpenAlexafffund
Guilherme Silva, Flávia Djenane Nunes de Souza, Letícia Marcorin, Álison Luís Eburnêo Pereira, Thássia Mayra Telles Carratto, Guilherme Debortoli, Maria Luiza Guimarães de Oliveira, Nádia Carolina de Aguiar Fracasso, Edilene Santos de Andrade, Eduardo Antônio Donadi, Heather Norton, Esteban J. Parra, Aguinaldo Luíz Simões, Erick C. Castelli, Celso Teixeira Mendes‐Junior

Bibliographic record

VenueForensic Science International Genetics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsUniversity of Toronto
FundersNational Institute of JusticeNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorAgilent Technologies
KeywordsSingle-nucleotide polymorphismSNPPopulationForensic geneticsForensic scienceGeneticsBiologySample (material)Identification (biology)DNA sequencingForensic identificationAlleleAllele frequencyComputational biologyDNA profilingSNP arrayMicrosatelliteGenotypeGeneDNAMedicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.119
GPT teacher head0.373
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations21
Published2019
Admission routes2
Has abstractno

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