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Regional Climates

2025· article· en· W4413142473 on OpenAlexaff
Anthony Argüez, P. Bissolli, C. Ganter, R. Martinez, A. Mekonnen, Laura A. Stevens, Zhiwei Zhu, William Agyakwah, Laura S. Aldeco, Eric J. Alfaro, Teddy Allen, Lincoln Muniz Alves, Jorge A. Amador, Bianca Ott Andrade, Parvaneh Asgharzadeh, Grinia Ávalos, Arti Bandgar, M. Yu. Bardin, Claire Basckenstrass, Marc Beauchemin, E. Bekele, Christine Berne, Rocky Bilotta, Oliver Bochníček, Kyle Brittain, Brandon Bukunt, Blanca Calderón, Jayaka Campbell, Ana Casella, Elise Chandler, Candice S. Charlton, Hua Chen, Vincent Y. S. Cheng, Leonardo A. Clarke, Kris Correa, Felipe Costa, Ana Paula Martins do Amaral Cunha, Veerle De Bock, Shiva Dindyal, Dashkhuu Dulamsuren, Paola Echeverría Garcés, Mithat Ekici, M. ElKharrim, Jhan Carlo Espinoza, Chris Fenimore, Brendan Forde, Steven Fuhrman, Artur Gevorgyan, Karin Gleason, S. Hakmi, Hugo G. Hidalgo, Bhaskar Jha, Guillaume Jumaux, K. Kabidi, Amin Fazl Kazemi, Michael Kendon, John Kennedy, Yelena Khalatyan, V. M. Khan, Mai Van Khiem, Natalia N. Korshunova, Katie Kowal, Andries Kruger, Mónika Lakatos, Hoang Phuc Lam, Waldo Lavado‐Casimiro, Renata Libonati, Xuefeng Liu, Rui Lü, Yuk Sing Lui, Gregor Macara, Jostein Mamen, José A. Marengo, Chris McBride, Caitlin Minney, Marjan Mohammadi, Aurel Moise, Jorge Molina‐Carpio, Martín Montenegro, Natali Mora, Ana Morata Gasca, A. E. Mostafa, T. Nomenjanahary, Yutong Pan, Reynaldo Pascual Ramírez, Patricia P. Rivera, M. Robjhon, Maarit Roebeling, Josyane Ronchail, F. Rubek, C. T. Sabeerali, Roberto Salinas, Hirotaka Sato, Zewdu Segele, Serhat Şensoy, Ji-In Seong, Julieta Serna Cuenca, Roopam Shukla, F. Sima, Bikram Singh, Adam Smith, Jacqueline Spence-Hemmings, Sandra Spillane, O. P. Sreejith, A. K. Srivastava, José Luis Stella, Tannecia S. Stephenson, Alif Akbar Syafrianno, Kiyotoshi Takahashi, Kazuto Takemura, Michael A. Taylor, Wassila M. Thiaw, Adrian Trotman, Maroš Turňa, Roderick van der Linden, Gerard van der Schrier, Cédric J. Van Meerbeeck, Ahad Vazifeh, R. Virasami, An Willems, Ying Yang, Peiqun Zhang

Bibliographic record

VenueBulletin of the American Meteorological Society · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Ecology and Soil Science
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsGeologyClimatologyEnvironmental scienceMeteorologyGeography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.050
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0500.020

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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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
Published2025
Admission routes1
Has abstractno

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