Pilot implementation of a newly developed bovine leukemia virus control program on 11 Alberta dairy farms
Notice bibliographique
Résumé
We developed a custom bovine leukemia virus (BLV) control program for the Alberta dairy industry, consisting of a risk assessment and a comprehensive list of best management practices (BMP) aimed at prevention of BLV transmission between cattle. This control program was implemented on 11 farms for approximately 1 yr. Blood samples were collected from all cattle ≥12 mo old, and serum was tested with a commercial ELISA. Risk assessments were performed on each farm, risk-connected on-farm management was identified, and management changes expected to prevent transmission of BLV between cattle were suggested by the first author and agreed upon with each farmer. Throughout the following year, all participating farmers were visited multiple times to identify and overcome barriers to implementation and to monitor progress. After approximately 1 yr of implementing BLV control, all cattle ≥12 mo old on farm with a negative or no previous test result were sampled, and the within-herd prevalence was determined. The median number of cattle on farm that were ≥12 mo was 195 (range 110-524). The initial prevalence averaged 39% (13-66%). On average, 5 BMP (3-7) were suggested to each farmer. On average, 4 BMP (1-7) were implemented. At the second sampling, the average within-herd prevalence of all animals that tested positive (including the previous sampling) was 36% (12-62%). Eight farms reduced their within-herd BLV prevalence, within-herd prevalence stayed constant on 1 farm, and it increased on 1 farm. The remaining farm terminated their participation before the second sampling. The number of seroconversions per farm ranged from 3 to 109, highlighting the success of some producers to minimize new infections. The risk assessment was proven to be a valuable tool to identify flaws in on-farm management, although risk assessment score was unrelated to the within-herd BLV prevalence. Finally, it appeared that implementation of BMP aimed at prevention of BLV transmission between cattle could reduce within-herd BLV prevalence when farmers committed to their implementation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».