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Record W4412587129 · doi:10.1007/s10584-025-03976-7

The attribution of human health outcomes to climate change: transdisciplinary practical guidance

2025· article· en· W4412587129 on OpenAlexaff
Kristie L. Ebi, Andy Haines, Roberto F. S. Andrade, Christofer Åström, Maurício L. Barreto, Ana Bonell, Nicholas Brink, Cyril Caminade, Colin J. Carlson, R. Carter, Paul Lester Chua, Guéladio Cissé, Felipe J. Colón‐González, Shouro Dasgupta, Luiz A C Galvao, Miguel Garrido Zornoza, Antonio Gasparrini, Georgiana Gordon‐Strachan, Shakoor Hajat, Sherilee L. Harper, Luke J. Harrington, Masahiro Hashizume, Jimmy Jaghoro Hilly, Vijendra Ingole, Linda Jacobson, Thandi Kapwata, Charles M. Keeler, Sean A. Kidd, Elizabeth Kimani‐Murage, R. K. Kolli, Sari Kovats, Shanshan Li, Rachel Lowe, Dann Mitchell, Kris A. Murray, Mark New, O E Ogunniyi, Sarah Perkins‐Kirkpatrick, Júlia Moreira Pescarini, B L Pineda Restrepo, Suani T. R. Pinho, Vanessa Prescott, Nicole Redvers, Sadie J. Ryan, Benjamin D. Santer, Carl‐Friedrich Schleussner, Jan C. Semenza, M. Taylor, Ludovic Temple, Sokhna Thiam, Wim Thiery, Adrian M. Tompkins, Sabine Undorf, Ana M. Vicedo‐Cabrera, Ke Wan, R. Warren, Candice Webster, Alistair Woodward, Caradee Y. Wright, Rupert Stuart-Smith

Bibliographic record

VenueClimatic Change · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of TorontoWestern UniversityCentre for Addiction and Mental HealthUniversity of Alberta
FundersWellcome Trust
KeywordsAttributionClimate changeHuman healthEnvironmental resource managementEnvironmental sciencePsychologyClimatologyEnvironmental healthMedicineSocial psychologyGeologyOceanography

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)0.173
metaresearch head score (Gemma)0.296
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.173
Threshold uncertainty score0.913

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1730.296
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0070.004
Science and technology studies0.0060.024
Scholarly communication0.0120.016
Open science0.0110.015
Research integrity0.0130.016
Insufficient payload (model declined to judge)0.0090.001

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 head0.193
GPT teacher head0.460
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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations15
Published2025
Admission routes1
Has abstractno

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