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Record W2955873362 · doi:10.1016/j.fsigen.2019.06.022

MAPlex - A massively parallel sequencing ancestry analysis multiplex for Asia-Pacific populations

2019· article· en· W2955873362 on OpenAlexfundno aff
Christopher Phillips, Dennis McNevin, Kenneth K. Kídd, Robert Lagacé, Sharon Wootton, M. de la Puente, A. Freire-Aradas, Ana Mosquera‐Miguel, Mayra Eduardoff, T.E. Gross, L. Dagostino, Daniel Power, Sarah H. Olson, Masaki Hashiyada, Carla Oz, Walther Parson, Peter M. Schneider, M.V. Lareu, Runa Daniel

Bibliographic record

VenueForensic Science International Genetics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsnot available
FundersAgencia Estatal de InvestigaciónEuropean Regional Development FundDepartment of Genetics, University of Alabama at BirminghamAlberta Foundation for the ArtsMinisterio de Agricultura, Pesca y AlimentaciónXunta de GaliciaConsellería de Economía, Emprego e Industria, Xunta de GaliciaAlzheimer's Foundation of AmericaYale School of MedicineYale UniversityHarvard Medical SchoolMinistério da Agricultura, Pecuária e Abastecimento
KeywordsBiologySingle-nucleotide polymorphismMassive parallel sequencingPopulationMultiplexEvolutionary biologyGeneticsAncestry-informative markerAllele frequencyEast AsiaAlleleGenotypeDNA sequencingGeographyChinaGeneArchaeologyDemography

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.002
metaresearch head score (Gemma)0.003
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.064
GPT teacher head0.356
Teacher spread0.292 · 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
GenreMethods

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

Citations67
Published2019
Admission routes1
Has abstractyes

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