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Power analyses to inform Duplex sequencing study designs for MutaMouse liver and bone marrow

2024· preprint· en· W4401671942 on OpenAlexaff
Elena Esina, Annette Dodge E, Andrew Williams, David Schuster, Danielle LeBlanc PM, Francesco Marchetti, Carole L. Yauk

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsHealth CanadaUniversity of Ottawa
Fundersnot available
KeywordsDuplex (building)Bone marrowPower (physics)Computer scienceComputational biologyBiologyMedicineInternal medicineGeneticsPhysicsDNA

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.235
metaresearch head score (Gemma)0.511
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.235
Threshold uncertainty score0.944

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2350.511
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0030.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0300.003

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 head0.066
GPT teacher head0.339
Teacher spread0.273 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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