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Record W4394158226 · doi:10.6084/m9.figshare.23962085

Additional file 5 of Host, reproductive, and lifestyle factors in relation to quantitative histologic metrics of the normal breast

2023· dataset· en· W4394158226 on OpenAlexaff
Mustapha Abubakar, Alyssa Klein, Shaoqi Fan, Scott M. Lawrence, Karun Mutreja, Jill E. Henry, Ruth M. Pfeiffer, Máire A. Duggan, Gretchen L. Gierach

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHost (biology)Relation (database)BiologyComputer scienceEcologyData mining

Abstract

fetched live from OpenAlex

Additional file 5. Table S1: Frequencies of missing values for each variable included the current analysis of 4108 women from the Komen Tissue Bank Study. Table S2: Associations of host, reproductive, and lifestyle factors with epithelium-to-stroma proportion among healthy women volunteers participating in the Komen Tissue Bank project, using missing values indicators, and following multiple imputation (N = 4108). Table S3: Associations of risk factors with epithelium-to-stroma proportion among healthy women volunteers participating in the Komen Tissue Bank project, with and without adjustment for age at first full-term birth (AFFB). Table S4: Associations of host, reproductive, and lifestyle factors with quantitative tissue composition metrics among healthy premenopausal women participating in the Komen Tissue Bank project (N = 2696). Table S5: Associations of host, reproductive, and lifestyle factors with quantitative tissue composition metrics among healthy postmenopausal women participating in the Komen Tissue Bank project (N = 1412).

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)0.022
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 score0.438
Threshold uncertainty score0.801

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.4380.071

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 head0.028
GPT teacher head0.265
Teacher spread0.237 · 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
Published2023
Admission routes1
Has abstractyes

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