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Record W2295754220 · doi:10.15454/dh0c-1a31

Quelle contribution de l'agriculture française à la réduction des émissions de gaz à effet de serre? Potentiel d'atténuation et coût de dix actions techniques. Résumé.

2013· preprint· fr· W2295754220 on OpenAlexaff
Sylvain Pellerin, Laure Bamière, Denis A. Angers, Fabrice Béline, Marc Benoît, Jean-Pierre Butault, Claire Chenu, Caroline Colnenne‐David, Stéphane de Cara, Nathalie Delame, Michel Doreau, Pierre Dupraz, Philippe Faverdin, Florence Garcia-Launay, Mélynda Hassouna, Catherine Hénault, Marie‐Hélène Jeuffroy, Katja Klumpp, Aurélie Metay, Dominic Moran, Sylvie Recous, Élisabeth Samson, Isabelle Savini, Lénaïc Pardon

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2013
Typepreprint
Languagefr
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsForestryEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

commanditaire de l'étude ici synthétisée: l’ADEME (Agence de l’Environnement et de la Maîtrise de l’Energie), le Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt (MAAF) et le Ministère de l'Ecologie, du Développement durable et de l'Energie (MEDDE)

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.252
Teacher spread0.236 · 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 designBench or experimental
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

Citations8
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueHAL (Le Centre pour la Communication Scientifique Directe)Same topicAgriculture and Rural Development ResearchFrench-language works237,207