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Record W3133922988 · doi:10.1038/s41467-021-22025-2

Author Correction: Continent-wide tree fecundity driven by indirect climate effects

2021· article· en· W3133922988 on OpenAlexaff
James S. Clark, Robert A. Andrus, Mélaine Aubry‐Kientz, Yves Bergeron, Michał Bogdziewicz, Don C. Bragg, Dale G. Brockway, Natalie L. Cleavitt, Susan Cohen, Benoı̂t Courbaud, Robert Daley, Adrian J. Das, Michael C. Dietze, Timothy J. Fahey, Istem Fer, Jerry F. Franklin, Catherine A. Gehring, Gregory S. Gilbert, Cathryn H. Greenberg, Qinfeng Guo, Janneke HilleRisLambers, Inés Ibáñez, Jill F. Johnstone, Christopher L. Kilner, Johannes M. H. Knops, Walter D. Koenig, Georges Künstler, Jalene M. LaMontagne, Kristin Legg, Jordan Luongo, James A. Lutz, Diana Macias, Eliot J. B. McIntire, Yassine Messaoud, Christopher M. Moore, Emily Moran, Jonathan A. Myers, Orrin Myers, Chase L. Núñez, Robert Parmenter, Sam Pearse, Scott M. Pearson, Renata Poulton‐Kamakura, Ethan Ready, Miranda D. Redmond, Chantal D. Reid, Kyle C. Rodman, C. Lane Scher, William H. Schlesinger, Amanda M. Schwantes, Erin Shanahan, Shubhi Sharma, Michael A. Steele, Nathan L. Stephenson, Samantha Sutton, Jennifer J. Swenson, Margaret Swift, Thomas T. Veblen, Amy V. Whipple, Thomas G. Whitham, Andreas P. Wion, Kai Zhu, Roman Zlotin

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of TorontoUniversity of SaskatchewanCanadian Sport Centre PacificUniversité du Québec en Abitibi-Témiscamingue
FundersAgence Nationale de la Recherche
KeywordsFecundityTree (set theory)Climate changeComputer scienceBiologyEcologyDemographyMathematicsCombinatoricsSociologyPopulation

Abstract

fetched live from OpenAlex

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22025-2

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)0.004
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.107
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.073
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.1070.055

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 head0.011
GPT teacher head0.278
Teacher spread0.267 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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