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BOUQUET - Une méthode pour évaluer les services rendus par les ateliers de volailles plein-air.

2022· preprint· en· W4282981795 on OpenAlexfundno aff
Geoffrey Chiron, Marion Pertusa, Manon Lesgourgues, Bertrand Méda, Laurence Fortun‐Lamothe, Fabien Liagre, Alexandre Parizel, Olivia Tavares, Hélène Gross, L. Dupuy, J. Protino, Jean-Marie Fontanet, Isabelle Bouvarel

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2022
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicLivestock and Poultry Management
Canadian institutionsnot available
FundersUniversité Jean Moulin Lyon 3Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementMinistry of Agriculture - Saskatchewan
KeywordsUnit (ring theory)Range (aeronautics)EngineeringMathematics

Abstract

fetched live from OpenAlex

In free-range poultry production, the management of the outdoor run is firstly designed to provide comfort to the animals. However, other services can be provided, such as improvement in income, contribution to direct and indirect jobs, or carbon storage and maintenance of biodiversity. In order to better consider them, these services need to be identified and quantified, which will make it possible a better reasoning of the outdoor run management. Developed by a group of experts, the service evaluation method, known as the BOUQUET method, focusing on free-range production units (meat, eggs and fat palmipeds), was discussed and adjusted with representatives of its potential users (farmers, technicians, advisers, etc.) in three production regions of France (West, South-East and South-West). The method considers 13 services divided into 5 categories. Those services are evaluated with a set of 29 indicators. The assessment takes half a day in three steps: i) mapping with an aerial picture of the site, ii) interview of the farmer and iii) measurements on the outdoor run studied. Data are then entered into a calculator which transforms them in scores for each service and provides a global representation of results by category of services. The analysis of these results allows the farmer to have a reflective approach of the management of his outdoor run, to establish a dialogue with his adviser, and to discuss an appropriate action plan according to his own constraints and objectives.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.227
Teacher spread0.201 · 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
Published2022
Admission routes1
Has abstractyes

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