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Record W2023168822 · doi:10.5194/acp-13-2793-2013

Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: a multi-model analysis

2013· article· en· W2023168822 on OpenAlexaff
Fortunat Joos, R. Roth, Jan S. Fuglestvedt, Glen P. Peters, I. G. Enting, Werner von Bloh, Victor Brovkin, Eleanor Burke, Michael Eby, Neil R. Edwards, Tobias Friedrich, Thomas L. Frölicher, Paul R. Halloran, Philip B. Holden, Chris Jones, Thomas Kleinen, Fred T. Mackenzie, Katsumi Matsumoto, Malte Meinshausen, Gian‐Kasper Plattner, Andy Reisinger, Joachim Segschneider, Gary Shaffer, M. Steinacher, Kuno Strassmann, Katsumasa Tanaka, Axel Timmermann, Andrew J. Weaver

Bibliographic record

VenueAtmospheric chemistry and physics · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Victoria
FundersEuropean CommissionSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungPrinceton UniversityMet OfficeDepartment for Environment, Food and Rural Affairs, UK GovernmentNational Science Foundation
KeywordsEnvironmental scienceGreenhouse gasCarbon dioxideAtmospheric sciencesGlobal warmingClimate changeGlobal temperatureClimatologyClimate modelAtmosphere (unit)Carbon cycleGlobal changeMeteorologyChemistryPhysicsEcosystem

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.213
Teacher spread0.206 · 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 designSimulation or modeling
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

Citations815
Published2013
Admission routes1
Has abstractyes

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