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Record W4407624467 · doi:10.1038/s41558-025-02279-0

Author Correction: Wildfires offset the increasing but spatially heterogeneous Arctic–boreal CO2 uptake

2025· article· en· W4407624467 on OpenAlexaff
Anna‐Maria Virkkala, Brendan M. Rogers, Jennifer D. Watts, Kyle A. Arndt, Stefano Potter, Isabel Wargowsky, Edward A. G. Schuur, Craig R. See, Marguerite Mauritz, Julia Boike, M. Syndonia Bret‐Harte, Eleanor Burke, Arden Burrell, Namyi Chae, Abhishek Chatterjee, Frédéric Chevallier, Torben R. Christensen, R. Commane, A. J. Dolman, Colin W. Edgar, Bo Elberling, Craig A. Emmerton, E. S. Euskirchen, Liang Feng, Mathias Goeckede, Achim Grelle, Manuel Helbig, David Holl, Järvi Järveoja, Sergey V. Karsanaev, Hideki Kobayashi, Lars Kutzbach, Junjie Liu, Ingrid T. Luijkx, Efrèn López‐Blanco, Kyle Lunneberg, Ivan Mammarella, Maija E. Marushchak, Mikhail Mastepanov, Yojiro Matsuura, Trofim Maximov, Lutz Merbold, Gesa Meyer, Mats B. Nilsson, Yosuke Niwa, Walter C. Oechel, Paul I. Palmer, Sang‐Jong Park, Frans‐Jan W. Parmentier, Matthias Peichl, Wouter Peters, Roman Petrov, William L. Quinton, Christian Rödenbeck, Torsten Sachs, Christopher Schulze, Oliver Sonnentag, Vincent L. St. Louis, Eeva‐Stiina Tuittila, Masahito Ueyama, Andrej Varlagin, Donatella Zona, Susan M. Natali

Bibliographic record

VenueNature Climate Change · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsWilfrid Laurier UniversityEnvironment and Climate Change CanadaDalhousie UniversityUniversité de MontréalUniversity of Alberta
Fundersnot available
KeywordsBorealEnvironmental scienceOffset (computer science)The arcticArcticClimatologyTaigaPhysical geographyAtmospheric sciencesOceanographyGeographyGeologyForestryComputer scienceArchaeology

Abstract

fetched live from OpenAlex

In the version of the article initially published, Mikhail Mastepanov’s second affiliation was incorrect and has now been amended to Oulanka Research Station, University of Oulu, Oulu, Finland 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.243
Teacher spread0.232 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2025
Admission routes1
Has abstractyes

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