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Record W4394078319 · doi:10.6084/m9.figshare.19589037

Additional file 2 of Genome-wide and transcriptome-wide association studies of mammographic density phenotypes reveal novel loci

2022· dataset· en· W4394078319 on OpenAlexaff
Hongjie Chen, Shaoqi Fan, Jennifer Stone, Deborah J. Thompson, Julie A. Douglas, Shuai Li, Christopher J. Scott, Manjeet K. Bolla, Qin Wang, Joe Dennis, Kyriaki Michailidou, Christopher Li, Ulrike Peters, John L. Hopper, Melissa C. Southey, Tú Nguyen‐Dumont, Tuong L. Nguyen, Peter A. Fasching, Annika Behrens, Gemma Cadby, Rachel A. Murphy, Kristan J. Aronson, Anthony Howell, Susan Astley, Fergus J. Couch, Janet E. Olson, Roger L. Milne, Graham G. Giles, Christopher A. Haiman, Gertraud Maskarinec, Stacey J. Winham, Esther M. John, Allison W. Kurian, Heather Eliassen, Irene L. Andrulis, D. Gareth Evans, William G. Newman, Per Hall, Kamila Czene, Anthony J. Swerdlow, Michael E. Jones, Marina Pollán, Pablo Fernández‐Navarro, Daniel McConnell, Vessela N. Kristensen, Joseph H. Rothstein, Pei Wang, Laurel A. Habel, Weiva Sieh, Alison M. Dunning, Paul D.P. Pharoah, Douglas F. Easton, Gretchen L. Gierach, Rulla M. Tamimi, Celine M. Vachon, Sara Lindström

Bibliographic record

VenueOpen MIND · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsPhenotypeTranscriptomeBiologyComputational biologyGeneticsMAMMOGRAPHIC DENSITYGenome-wide association studyGeneSingle-nucleotide polymorphismGenotypeMammographyBreast cancerGene expressionCancer

Abstract

fetched live from OpenAlex

Additional file 2. Supplementary Tables.

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)0.002
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.622
Threshold uncertainty score0.539

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.6220.099

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 head0.026
GPT teacher head0.279
Teacher spread0.253 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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