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Record W2912294768 · doi:10.1016/j.cliser.2019.01.004

Adjusting climate model bias for agricultural impact assessment: How to cut the mustard

2019· article· en· W2912294768 on OpenAlexaff
Stefano Galmarini, Alex J. Cannon, Andrej Ceglar, Ole B. Christensen, Nathalie de Noblet‐Ducoudré, Frank Dentener, Francisco J. Doblas‐Reyes, Alessandro Dosio, José Manuel Gutiérrez, Maialen Iturbide, Martin Jury, Stefan Lange, Harilaos Loukos, A. Maiorano, Douglas Maraun, Seth McGinnis, Grigory Nikulin, Angelo Riccio, Enrique Sánchez, Efisio Solazzo, Andrea Toreti, Mathieu Vrac, Matteo Zampieri

Bibliographic record

VenueClimate Services · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsGDG EnvironnementEnvironment and Climate Change Canada
FundersEnvironmental Security Technology Certification ProgramAgence Nationale de la RechercheU.S. Department of EnergyU.S. Department of Defense
KeywordsAgricultureAgency (philosophy)Political scienceFunding AgencyRegional scienceForestryHumanitiesGeographySociologySocial scienceArchaeology

Abstract

fetched live from OpenAlex

come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire

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 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)0.062
metaresearch head score (Gemma)0.292
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.329

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.292
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0040.006
Open science0.0030.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0060.002

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 head0.052
GPT teacher head0.298
Teacher spread0.246 · 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

Citations45
Published2019
Admission routes1
Has abstractyes

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