BOUQUET - Une méthode pour évaluer les services rendus par les ateliers de volailles plein-air.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".