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A bioinformatics approach to ascertaining the rarity of HLA alleles

2009· article· en· W2074036828 on OpenAlexaff
Derek Middleton, Frank J. Gonzalez, Marcelo Fernández-Viña, J.‐M. Tiercy, Steven G. E. Marsh, Michael T. Aubrey, Maria Gabriela Parenti Bicalho, A. Canossi, V. Carter, Steven Cate, Franca Rosa Guerini, P Loiseau, M. Martinetti, M. E. Moraes, Valérie Morales, Juha Peräsaari, Michelle Setterholm, Maggie Sprague, Stavros Tavoularis, M. Torres, Sílvia Vidal, Campbell S. Witt, G. Wohlwend, Kuo‐Liang Yang

Bibliographic record

VenueTissue Antigens · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicvaccines and immunoinformatics approaches
Canadian institutionsCanadian Blood Services
Fundersnot available
KeywordsHuman leukocyte antigenAlleleMajor histocompatibility complexHistocompatibilityBiologyGeneticsHistocompatibility TestingMechanism (biology)Computational biologyGenealogyGeneAntigenHistoryPhilosophyEpistemology

Abstract

fetched live from OpenAlex

A project of the 15th International Histocompatibility Workshop examined the rarity of human leukocyte antigen (HLA) alleles. A section was constructed in the website, www.allelefrequencies.net to contain this data from different sources. A mechanism to search the data was implemented for use by any individual.

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)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.005
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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.

Opus teacher head0.018
GPT teacher head0.259
Teacher spread0.241 · 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 designSimulation or modeling
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

Citations20
Published2009
Admission routes1
Has abstractyes

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