Le traitement ciblé-sélectif des bovins, ovins et caprins contre les strongles gastro-intestinaux
Bibliographic record
Abstract
Grazing ruminants are always infested by gastrointestinal nematodes. Whatever the animal supply chain, the answer is often an insufficiently reasoned use of anthelmintics, which involves a development of resistant parasites. Nowadays, it is necessary to better use these treatments. The project aimed at answering two major questions: “when to treat” and “who to treat” in dairy cows, sheep and goats, and to understand motivations of farmers and advisers. It appears that, for dairy cows, it would be better to treat at the beginning of the housing period, only inadequately immunized young cows that are severely exposed to parasites in pasture. For small ruminants, the mixing coproscopy provides a good estimation of the level of excretion of a group of animals and enables to target the time of intervention. Moreover, at the individual level, the criteria used by goat and cow farmers permit to correctly select animals to be treated. For cattle and goats, grazing parasitic risk periods can be estimated by an expert system. A. Duvauchelle-Waché et al. 136 Innovations Agronomiques 55 (207), 135-153 Finally, it appears that actors of the cattle industry are less aware of the targeted-selective treatment than the goat sector, but raising their awareness is possible.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".