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Record W4396707709 · doi:10.1038/s41592-024-02296-5

Author Correction: brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

2024· erratum· en· W4396707709 on OpenAlexaff
Soichi Hayashi, Bradley Caron, Anibal Sólon Heinsfeld, Sophia Vinci‐Booher, Brent McPherson, Daniel Bullock, Giulia Bertò, Guiomar Niso, Sandra Hanekamp, Daniel Levitas, Kimberly L. Ray, A. Mackenzie, Paolo Avesani, Lindsey Kitchell, Josiah K. Leong, Filipi N. Silva, Serge Koudoro, Hanna E. Willis, Jasleen K. Jolly, Derek Pisner, Taylor R. Zuidema, Jan W. Kurzawski, Kyriaki Mikellidou, Aurore Bussalb, Maximilien Chaumon, Nathalie George, Chris Rorden, Conner Victory, Dheeraj Bhatia, Dogu Baran Aydogan, Fang‐Cheng Yeh, Franco Delogu, Javier Guaje, Jelle Veraart, Jeremy Fischer, Joshua Faskowitz, Ricardo Fábrega, David Hunt, S. P. Mc Kee, Shawn T. Brown, Stephanie Heyman, Vittorio Iacovella, Amanda F. Mejia, Daniele Marinazzo, R. Cameron Craddock, Emanuale Olivetti, Jamie L. Hanson, Eleftherios Garyfallidis, Dan Stanzione, James P. Carson, Robert Henschel, David Y. Hancock, Craig A. Stewart, David M. Schnyer, Damian Eke, Russell A. Poldrack, Steffen Bollmann, Ashley Stewart, Holly Bridge, Ilaria Sani, Winrich A. Freiwald, Aina Puce, Nicholas Port, Franco Pestilli

Bibliographic record

VenueNature Methods · 2024
Typeerratum
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsCloud computingOpen sourceComputer scienceData scienceNeuroscienceComputational biologyWorld Wide WebBiologyOperating systemSoftware

Abstract

fetched live from OpenAlex

In the version of the article initially published, Steffen Bollmann’s surname was misspelled (as Bollman) and has now been corrected in the HTML and PDF 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)0.005
metaresearch head score (Gemma)0.080
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
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 score0.996
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.080
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.003
Scholarly communication0.0050.003
Open science0.0040.003
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0830.069

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 head0.196
GPT teacher head0.504
Teacher spread0.308 · 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
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

Citations2
Published2024
Admission routes1
Has abstractyes

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