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Record W4408103117 · doi:10.1016/j.lanepe.2025.101257

Europe's climate leadership in an ‘America first’ era

2025· article· en· W4408103117 on OpenAlexaff
Kim Robin van Daalen, Hedi Katre Kriit, José Chen-Xu, Jan C. Semenza, Maria Nilsson, Niheer Dasandi, Anil Markandya, Josep M. Antó, Joacim Rocklöv, Cathryn Tonne

Bibliographic record

VenueThe Lancet Regional Health - Europe · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersHORIZON EUROPE Framework ProgrammeEuropean CommissionWellcome Trust
KeywordsPolitical scienceClimatologyGeology

Abstract

fetched live from OpenAlex

We thank the global Lancet Countdown and the Wellcome Trust (grant no. 304972/Z/23/Zhan) for their financial and technical support. We acknowledge funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101057131 (Horizon Europe project CATALYSE, https:// catalysehorizon.eu/) and grant agreement No. 101057554 (Horizon Europe project IDAlert, https://idalertproject.eu). CATALYSE and IDAlert are part of the EU climate change and health cluster (https:// climate-health.eu). We would also like to thank Magnus Wahlberg for expert advice on creating the visualisations in Python.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.454
GPT teacher head0.344
Teacher spread0.110 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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 abstractyes

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