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Record W4414967974 · doi:10.1038/s41550-025-02698-1

Author Correction: Dynamical dark energy in light of the DESI DR2 baryonic acoustic oscillations measurements

2025· article· en· W4414967974 on OpenAlexaff
Gangxu Gu, Xiaoma Wang, Yuting Wang, Gong‐Bo Zhao, Levon Pogosian, K. Koyama, J. A. Peacock, Zheng Cai, Jorge L. Cervantes–Cota, Mustapha Ishak, Arman Shafieloo, Ruiyang Zhao, S. P. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, Shaun Cole, Axel de la Macorra, Arnaud de Mattia, P. Doel, Simone Ferraro, J. E. Forero-Romero, E. Gaztañaga, Satya Gontcho A Gontcho, G. Gutiérrez, ChangHoon Hahn, Cullan Howlett, R. Kehoe, D. Kirkby, Jean‐Paul Kneib, O. Lahav, Martin Landriau, L. Le Guillou, Alexie Leauthaud, M. E. Levi, Marc Manera, Aaron Meisner, R. Miquel, John Moustakas, A. Muñoz-Gutiérrez, S. Nadathur, Jeffrey A. Newman, N. Palanque‐Delabrouille, Will J. Percival, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Lado Samushia, E. Sánchez, David J. Schlegel, Hee‐Jong Seo, David Sprayberry, G. Tarlé, Michael Walther, Benjamin Alan Weaver, Pauline Zarrouk, Cheng Zhao, Rongpu Zhou, Hu Zou

Bibliographic record

VenueNature Astronomy · 2025
Typearticle
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsPerimeter InstituteUniversity of WaterlooSimon Fraser University
Fundersnot available
KeywordsDark energyBaryon acoustic oscillationsEnergy (signal processing)BaryonDark matter

Abstract

fetched live from OpenAlex

In the version of the article initially published, the surnames of authors Jorge L. Cervantes-Cota, Satya Gontcho A Gontcho and Nathalie Palanque-Delabrouille appeared incorrectly (as Cervantes-Cot, Gontcho A Gontch and Palanque-Dela, respectively). The errors have 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)0.002
metaresearch head score (Gemma)0.058
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 score0.073
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.058
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0730.040

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 head0.008
GPT teacher head0.253
Teacher spread0.245 · 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
Published2025
Admission routes1
Has abstractyes

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