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Record W6922025696 · doi:10.1051/npvelsa/36059/pdf

L’accord Union européenne - Canada : quels risques pour les productions animales européennes ?

2017· article· fr· W6922025696 on OpenAlexaboutno aff

Bibliographic record

VenueSpringer Link (Chiba Institute of Technology) · 2017
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicPharmacological Effects and Assays
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal productionWestern europeEuropean unionLatin Americans

Abstract

fetched live from OpenAlex

\nLe CETA (Comprehensive Economic and Trade Agreement) voté par le parlement européen en février 2017 va avoir des retombées certaines sur les agricultures européennes, notamment par la baisse forte de droits de douane encore importants.\n\nC’est le cas en particulier en viande porcine et bovine, avec une forte hausse des contingents offerts à droits de douane nuls.\n\nLe CETA offre également de nouvelles possibilités de révision à la baisse des normes sanitaires, phytosanitaires et environnementales, notamment l’interdiction de certaines substances de décontamination sur les produits d'origine animale, l’interdiction de traitement à la ractopamine des animaux, l’interdiction des activateurs de croissance hormonaux pour les bovins viande, les relatives restrictions dans l’importation, la consommation et la production d’OGM.\n\n\n\nUn rapport très récent, effectué par des chercheurs des instituts techniques de l’élevage de ruminants, du porc et d’AgroParisTech montre ainsi que cet accord risque de fragiliser fortement les productions européennes de viande porcine et bovine, par des imports supplémentaires considérables de viande à droits de douane nuls à certaines périodes.\n\nLa concurrence outre-atlantique se ferait notamment sur certaines pièces de découpe, comme les côtes et aloyaux en viande bovine. Enfin, ces effets s’ajouteront à ceux de dizaines d’autres accords de libre-échange conclus ou en cours de négociation par l’UE avec d’autres pays ou régions du monde.\n\n\t\t\t

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.257
Teacher spread0.226 · 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
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

Citations0
Published2017
Admission routes1
Has abstractyes

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