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Record W3107871550 · doi:10.1038/s41561-020-00663-3

Opportunities and challenges in using remaining carbon budgets to guide climate policy

2020· article· en· W3107871550 on OpenAlexafffund
H. Damon Matthews, Katarzyna Tokarska, Zebedee Nicholls, Joeri Rogelj, Josep G. Canadell, Pierre Friedlingstein, Thomas L. Frölicher, Piers Forster, Nathan P. Gillett, Tatiana Ilyina, Robert B. Jackson, Chris Jones, Charles D. Koven, Reto Knutti, Andrew H. MacDougall, Malte Meinshausen, Nadine Mengis, Roland Séférian, Kirsten Zickfeld

Bibliographic record

VenueNature Geoscience · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsSt. Francis Xavier UniversityEnvironment and Climate Change CanadaSimon Fraser UniversityConcordia University
FundersHorizon 2020 Framework ProgrammeConcordia UniversityNatural Sciences and Engineering Research Council of CanadaEuropean CommissionSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungGordon and Betty Moore FoundationMet OfficeSimon Fraser UniversityDepartment for Environment, Food and Rural Affairs, UK GovernmentNational Science Foundation
KeywordsLimitingClimate policyCarbon fibersClimate changeEnvironmental scienceCarbon priceNatural resource economicsGlobal warmingEconomicsComputer scienceEcologyEngineering

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)0.030
metaresearch head score (Gemma)0.084
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.084
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0110.014
Open science0.0030.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.337
GPT teacher head0.317
Teacher spread0.021 · 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
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

Citations142
Published2020
Admission routes2
Has abstractno

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