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Record W2900959981 · doi:10.1038/s41467-018-07407-3

Author Correction: The prognostic effects of somatic mutations in ER-positive breast cancer

2018· erratum· en· W2900959981 on OpenAlexaff
Obi L. Griffith, Nicholas C. Spies, Meenakshi Anurag, Malachi Griffith, Jingqin Luo, Dongsheng Tu, Belinda Yeo, Jason Kunisaki, Christopher A. Miller, Kilannin Krysiak, Jasreet Hundal, Benjamin J. Ainscough, Zachary L. Skidmore, Katie M. Campbell, Runjun D. Kumar, Catrina C. Fronick, Lisa Cook, Jacqueline Snider, Sherri R. Davies, Shyam M. Kavuri, Eric C. Chang, Vincent Magrini, David E. Larson, Robert S. Fulton, Shuzhen Liu, Samuel Leung, David Voduc, Ron Bose, Mitch Dowsett, Richard K. Wilson, Torsten O. Nielsen, Elaine R. Mardis, Matthew J. Ellis

Bibliographic record

VenueNature Communications · 2018
Typeerratum
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsUniversity of British Columbia
FundersNational Institute for Health and Care Research
KeywordsSomatic cellConfidence intervalBreast cancerDepictionGermline mutationMutationOncologyStatisticsCancerComputer scienceInternal medicineMedicineGeneticsBiologyMathematicsPhilosophyLinguisticsGene

Abstract

fetched live from OpenAlex

The original version of this Article contained errors in the depiction of confidence intervals in the NF1 BCSS data illustrated in Figure 3b. These have now been corrected in both the PDF and HTML versions of the Article. The incorrect version of Figure 3b is presented in the associated Author Correction.

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)0.004
metaresearch head score (Gemma)0.061
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0500.020

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 head0.012
GPT teacher head0.336
Teacher spread0.324 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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