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Record W3125895423

Efficiences technique et environnementale en agriculture : le cas du bassin de la rivière Chaudière au Québec

2014· preprint· fr· W3125895423 on OpenAlexaboutno aff
Lota D. Tamini, Bruno Larue, Gale E. West

Bibliographic record

VenueRePEc: Research Papers in Economics · 2014
Typepreprint
Languagefr
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
Fundersnot available
KeywordsForestryPolitical scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

Malgre l’imposition de normes environnementales strictes au Quebec, l’impact des activites agricoles sur la qualite de l’eau demeure preoccupant notamment dans la region de Chaudiere-Appalaches. Cette region est intensive en productions animale et vegetale ce qui entraine des surplus de phosphore, d’azote et de sediments. Cette etude a pour objectif que d’analyser l’efficience technique et l'efficience environnementale des producteurs agricoles du bassin de la riviere Chaudiere localise au Sud de la ville de Quebec. Nous adoptons une approche stochastique parametrique appliquee aux fonctions de distance. Les donnees utilisees portent sur 210 fermes agricoles et les resultats obtenus montrent qu’il existe une forte correlation entre les deux efficiences. De plus, comme le montrent d’autres etudes, la performance environnementale entraine des couts additionnels au niveau des exploitations agricoles.

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)0.040
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch, Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0400.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0010.000
Open science0.0040.003
Research integrity0.0010.004
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 head0.022
GPT teacher head0.350
Teacher spread0.328 · 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; both teacher heads agree on what is shown here.

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
Published2014
Admission routes1
Has abstractyes

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