Evaluation du bien-être animal d'un troupeau de vaches allaitantes de race Maraîchine, au sein de l'unité expérimentale INRAE de Saint Laurent de la Prée
Bibliographic record
Abstract
The aims of the internship were (1) processing and analysing results of Welfare Quality diagnostic carried out at Saint-Laurent de la Prée unit on January 2020, (2) making links between results and breeding system and (3) analysing the QBA repeatability (Qualitative Behaviour Assessment) trough 4 observers. These aims were achieved through 3 literature reviews, simulations on Welfare Quality ® website and astatistical reviewon R. Results show farm’s welfare level as “Enhanced” according to the 4 welfare categories distinguished Welfare Quality diagnostic. Principles scores are all good (the 4 are between 64 and 77) and steady (all are “enhanced”), which means that all aspects of welfare are considered in the farm. Only two criterion score poorly: absence ofprolonged hunger (54,8) and comfort around resting (51,4). Morphologies variability may due to Maraîchines hardy breedand comfort around resting may be influenced by quality and frequency of mulching, and running of drinking trough. Protocol boundaries areprecisely to rely on dairy cows and not to considerdiversity of beef cattle situations and breed. QBA analysis showed a difference in the observers’perception of animals’behaviour and its consequences on welfare. Proposals for upgrading and following-up animal welfare have been set out.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".