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Record W6973680798 · doi:10.57745/ypcgrw

06_GWAS_log.magnitude.html

2024· dataset· en· W6973680798 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRecherche Data Gouv France · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsInuit Tapiriit Kanatami
Fundersnot available
KeywordsLinear regressionSelection (genetic algorithm)Feature selectionGenome-wide association studyLinear modelModel selectionBayesian probability

Abstract

fetched live from OpenAlex

GWAS analysis. Three types of GWAS were performed: 1) An SNP-by-SNP model implemented in R/MM4LMM version 2.1.0 (Laporte et al., 2022) based on a linear mixed model was used to test the significance of the effect of each SNP; 2) the multi-locus mixed model MLMM (Segura et al., 2012), which jointly analyses all SNPs to handle LD while selecting a subset of SNPs with a stepwise regression procedure ; 3) SNP selection in a Bayesian setting (Flutre et al., 2022) fitted with the variational algorithm implemented in R/varbvs version 2.5.7 (Carbonetto & Stephens, 2012).

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0130.006
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0030.582

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 head0.445
GPT teacher head0.471
Teacher spread0.026 · 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

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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