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Record W2030067801 · doi:10.1186/1753-6561-1-s1-s1

Genetic Analysis Workshop 15: gene expression analysis and approaches to detecting multiple functional loci

2007· article· en· W2030067801 on OpenAlexafffundabout
Heather J. Cordell, Mariza de Andrade, Marie‐Claude Babron, Christopher W. Bartlett, Joseph Beyene, Heike Bickeböller, Robert Culverhouse, L. Adrienne Cupples, E. Warwick Daw, Josée Dupuis, Catherine T. Falk, Saurabh Ghosh, Katrina A.B. Goddard, Ellen L. Goode, Elizabeth R. Hauser, Lisa J. Martin, María Martínez, Kari E. North, Nancy L. Saccone, Silke Schmidt, William Tapper, Duncan C. Thomas, David Tritchler, Veronica J. Vieland, Ellen M. Wijsman, Marsha Wilcox, John S. Witte, Qiong Yang, Andreas Ziegler, Laura Almasy, Jean W. MacCluer

Bibliographic record

VenueBMC Proceedings · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsOntario Institute for Cancer ResearchHospital for Sick Children
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of General Medical SciencesNational Human Genome Research InstituteNational Institute on AgingNational Institutes of HealthNational Heart, Lung, and Blood InstituteGenome Canada
KeywordsMedicineGerontologyBioinformaticsFamily medicineBiology

Abstract

fetched live from OpenAlex

National Institutes of Health (AR44422, N01-AR-7-2232, 5R01-HL049609-14, IR01-AG021917-01A1); Genome Canada and Associations AFP; Polyarctique-Groupe Taitbout; Rhumatisme et Travail; Arthritis Research Campaign; Unversity of Minnesota; Minnesota Supercomputing Institute; National Institute of General Medical Sciences (R01 GM31575)

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)0.017
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.006
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0060.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0170.010

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 head0.055
GPT teacher head0.256
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations9
Published2007
Admission routes3
Has abstractyes

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