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Record W2899455707 · doi:10.1038/s41467-018-07155-4

Author Correction: Pan-cancer deconvolution of tumour composition using DNA methylation

2018· erratum· en· W2899455707 on OpenAlexaff
Ankur Chakravarthy, Andrew J.S. Furness, Kroopa Joshi, Ehsan Ghorani, Kirsty Ford, Matthew J. Ward, Emma V. King, Matt Lechner, Teresa Marafioti, Sergio A. Quezada, Gareth J. Thomas, Andrew Feber, Tim R. Fenton

Bibliographic record

VenueNature Communications · 2018
Typeerratum
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsPrincess Margaret Cancer Centre
FundersMedical Research CouncilRosetrees TrustProstate Cancer UKCancer Research UK
KeywordsDeconvolutionDNACode (set theory)Composition (language)Computational biologyDNA methylationComputer scienceAlgorithmBiologyGeneticsGenePhilosophy

Abstract

fetched live from OpenAlex

The original version of this Article contained an error in Figure 4. In panel a, the colour code for hot and cold clusters was inadvertently inverted. In the correct version of panel a, the hot clusters are blue and the cold clusters are yellow. This error has now been corrected in both the PDF and HTML versions of the Article.

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)0.043
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 score0.104
Threshold uncertainty score0.348

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.043
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.1040.041

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 head0.345
Teacher spread0.317 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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