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Record W4405816166 · doi:10.1038/s43247-024-01938-y

Author Correction: Expert elicitation of state shifts and divergent sensitivities to climate warming across northern ecosystems

2024· article· en· W4405816166 on OpenAlexaff
Émilie Saulnier‐Talbot, Éliane Duchesne, Dermot Antoniades, Dominique Arseneault, Christine Barnard, Dominique Berteaux, Najat Bhiry, Frédéric Bouchard, Stéphane Boudreau, Kévin Cazelles, Jérôme Comte, Madeleine-Zoé Corbeil-Robitaille, Steeve D. Côté, Raoul‐Marie Couture, Guillaume de Lafontaine, Florent Dominé, Dominique Fauteux, Daniel Fortier, Michelle Garneau, Gilles Gauthier, Dominique Gravel, Isabelle Laurion, Martin Lavoie, Nicolas Lecomte, Pierre Legagneux, Esther Lévesque, Marie‐José Naud, Michel Paquette, Serge Payette, Reinhard Pienitz, Milla Rautio, Alexandre Roy, Alain Royer, M. Simard, Warwick F. Vincent, Joël Bêty

Bibliographic record

VenueCommunications Earth & Environment · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsSquamish NationUniversité du Québec à Trois-RivièresUniversité de SherbrookeCenter for Northern StudiesUniversité de MonctonCanadian Museum of NatureUniversité du Québec à MontréalUniversité du Québec à RimouskiUniversité du Québec à ChicoutimiUniversité de MontréalInstitut National de la Recherche ScientifiqueArcticNetUniversité Laval
Fundersnot available
KeywordsEcosystemGlobal warmingEnvironmental scienceClimate changeClimatologyEcologyEnvironmental resource managementBiologyGeology

Abstract

fetched live from OpenAlex

Correction to: Communications Earth & Environment https://doi.org/10.1038/s43247-024-01791-z , published online 25 October 2024

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)0.006
metaresearch head score (Gemma)0.153
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.128
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.153
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0030.003
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.1280.054

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 head0.049
GPT teacher head0.282
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 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
GenreEditorial

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

Citations1
Published2024
Admission routes1
Has abstractyes

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