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Record W2919782064 · doi:10.1038/s41467-019-08814-w

Publisher Correction: Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set

2019· erratum· en· W2919782064 on OpenAlexaff
J. Khadake, Birgit Meldal, Simona Panni, D. Thorneycroft, K. Van Roey, S. Abbani, Łukasz Salwiński, Matteo Pellegrini, Marta Iannuccelli, Luana Licata, Gianni Cesareni, Bernd Roechert, Alan Bridge, M. G. Ammari, F. McCarthy, F. Broackes-Carter, N. Campbell, Anna N. Melidoni, M. Rodríguez-López, Ruth C. Lovering, S. Jagannathan, Carol Chen, David J. Lynn, Sylvie Ricard‐Blum, Uma Mahadevan, Arathi Raghunath, Noemí del‐Toro, Margaret Duesbury, M. H. J. Koch, Livia Perfetto, Anjali Shrivastava, David Ochoa, Omar Wagih, Janet Piñero, Max Kotlyar, Chiara Pastrello, Pedro Beltrão, Laura I. Furlong, Igor Jurišica, Henning Hermjakob, Sandra Orchard, Pablo Porras

Bibliographic record

VenueNature Communications · 2019
Typeerratum
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaDiscovery CentreUniversity Health Network
Fundersnot available
KeywordsSet (abstract data type)Computer scienceVariation (astronomy)Computational biologyOrder (exchange)Data setLibrary scienceInformation retrievalData scienceBiologyArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

In the original HTML version of this Article, the order of authors within the author list was incorrect. The IMEx Consortium contributing authors were incorrectly listed as the last author and should have been listed as the first author. This error has been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.

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)0.207
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 score0.129
Threshold uncertainty score0.430

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.207
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.012
Science and technology studies0.0030.002
Scholarly communication0.0080.003
Open science0.0030.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.1290.054

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 head0.494
GPT teacher head0.593
Teacher spread0.099 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicscientometrics and bibliometrics researchFrench-language works237,207