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Record W4205595504 · doi:10.4049/jimmunol.2100961

Minimal Information about MHC Multimers (MIAMM)

2022· article· en· W4205595504 on OpenAlexaff
Randi Vita, Apurva Mody, James A. Overton, Søren Buus, Stephen T. Haley, Alessandro Sette, Vamsee Mallajosyula, Mark M. Davis, Dale L. Long, Richard A. Willis, Bjoern Peters, John D. Altman

Bibliographic record

VenueThe Journal of Immunology · 2022
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsInstitute of Infection and ImmunityNoNO (Canada)
FundersNational Institute of Allergy and Infectious DiseasesNational Human Genome Research InstituteNational Institute on Drug AbuseOffice of Extramural Research, National Institutes of HealthNational Heart, Lung, and Blood InstituteNational Institutes of HealthYerkes National Primate Research Center, Emory UniversityCenter for AIDS Research, Emory University
KeywordsTerminologyMajor histocompatibility complexDocumentationComputer scienceInformation retrievalWorld Wide WebComputational biologyBiologyProgramming languageImmunologyAntigen

Abstract

fetched live from OpenAlex

With the goal of improving the reproducibility and annotatability of MHC multimer reagent data, we present the establishment of a new data standard: Minimal Information about MHC Multimers (https://miamm.lji.org/). Multimers are engineered reagents composed of a ligand and a MHC, which can be represented in a standardized format using ontology terminology. We provide an online Web site to host the details of the standard, as well as a validation tool to assist with the adoption of the standard. We hope that this publication will bring increased awareness of Minimal Information about MHC Multimers and drive acceptance, ultimately improving the quality and documentation of multimer data in the scientific literature.

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)0.012
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0090.007
Science and technology studies0.0020.001
Scholarly communication0.0050.009
Open science0.0030.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.007

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 head0.013
GPT teacher head0.268
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 designTheoretical or conceptual
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

Citations2
Published2022
Admission routes1
Has abstractyes

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