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Record W3018713651 · doi:10.1038/s41467-019-08563-w

Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

2019· erratum· en· W3018713651 on OpenAlexafffund
Lena Maier‐Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Godau, Tal Arbel, Hrvoje Bogunović, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno März, Oskar Maier, Klaus Maier‐Hein, Bjoern Menze, Henning Müller, Peter Neher, Wiro J. Niessen, Nasir Rajpoot, G Sharp, Korsuk Sirinukunwattana, Stefanie Speidel, Christian Stock, Danail Stoyanov, Abdel Aziz Taha, Fons van der Sommen, Ching‐Wei Wang, Marc André Weber, Guoyan Zheng, Pierre Jannin, Annette Kopp‐Schneider

Bibliographic record

VenueNature Communications · 2019
Typeerratum
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsMcGill University
FundersScience and Engineering Faculty, Queensland University of TechnologyInstitut National de la Santé et de la Recherche MédicaleEngineering and Physical Sciences Research CouncilMedical Research CouncilHaute école Spécialisée de Suisse OccidentaleMasarykova UniverzitaTechnische Universität MünchenTechnische Universiteit EindhovenRadboud UniversiteitQueensland University of TechnologyMassachusetts General HospitalMcGill UniversityUniversity of LeedsNational Taiwan UniversityUniversity of WarwickUniversity of OxfordUniversity College LondonUniversität zu LübeckDeutsches KrebsforschungszentrumJohns Hopkins UniversityNational Taiwan University of Science and TechnologyVanderbilt UniversityTechnische Universität WienUniversity of Bern
KeywordsColumn (typography)Table (database)Computer scienceImage (mathematics)Information retrievalArtificial intelligenceData miningTelecommunications

Abstract

fetched live from OpenAlex

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in 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)0.013
metaresearch head score (Gemma)0.256
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.256
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.007
Science and technology studies0.0040.003
Scholarly communication0.0070.003
Open science0.0030.003
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0950.055

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 head0.342
Teacher spread0.327 · 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 designNot applicable
DomainEvaluation
GenreCommentary

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

Citations11
Published2019
Admission routes2
Has abstractyes

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