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Record W4221109923 · doi:10.1038/s41598-022-08800-1

Author Correction: Integrated genomics point to immune vulnerabilities in pleural mesothelioma

2022· erratum· en· W4221109923 on OpenAlexaff
Anca Năstase, Amit Kumar Mandal, Shir Kiong Lu, Hima Anbunathan, Déborah Morris-Rosendahl, Yu Zhi Zhang, Xiaoming Sun, Spyridon Gennatas, Robert C. Rintoul, Matthew Edwards, Alex Bowman, Tatyana Chernova, T. Benepal, Eric Lim, Anthony Newman Taylor, Andrew G. Nicholson, Sanjay Popat, Anne E. Willis, Marion MacFarlane, Mark Lathrop, A. Bowcock, Miriam F. Moffatt, William Cookson

Bibliographic record

VenueScientific Reports · 2022
Typeerratum
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsMcGill Genome Centre
Fundersnot available
KeywordsMesotheliomaGenomicsComputational biologyComputer sciencePoint (geometry)Data scienceBioinformaticsMedicineBiologyPathologyGeneticsGenomeGene

Abstract

fetched live from OpenAlex

In the Results section, under subheading ‘High level of VISTA is frequent in epithelioid mesothelioma and its expression level correlates with Hedgehog and immune pathway components’,

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)0.003
metaresearch head score (Gemma)0.032
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 score0.028
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0280.018

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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueScientific Reports→Same topicOccupational and environmental lung diseases→French-language works237,207→