0120 Mortality risk factors for calves entering a multilocation white veal farm in Ontario
Notice bibliographique
Résumé
Mortality in preweaned dairy breed calves of both sexes represents a potential welfare issue and a source of economic loss for the industries involved. While morbidity and mortality in veal production has been described, this work reflects a wide range of management practices and requirements throughout the world. In preweaned dairy heifers, rates of morbidity and mortality can also range dramatically, due in large part to differing management strategies. It has been over two decades since mortality in veal calves in Ontario was last described. The objective of this retrospective cohort study was to determine if recorded on-arrival data collected from a large white veal farm could be used as predictors of mortality. Data was collected from 10,910 calves entering seven barns of a single white veal farm, all locations of barns within Ontario, from 1 Jan. to 31 Dec. 2014. Calves were followed until death or marketing; no calves were culled during the year. Three logistic regression models were used to determine the effects of weight on arrival, season of arrival, supplier, sex, barn, and standardized purchase price on the risk of overall mortality, mortality in the first 21 d after arrival, and mortality after the first 21 d. In the overall mortality model, significant associations (P < 0.05) were seen with season, barn, supplier and weight, with lighter weight calves arriving in winter being at increased odds of mortality. The early mortality model contained significant (P < 0.05) associations with weight, season, barn, supplier and tended (P < 0.10) to have an association with standardized price; lighter weight calves arriving in winter at lower prices were at increased odds of mortality. The late mortality model had significant (P < 0.05) associations with season of arrival, barn and supplier. While not a proxy for body condition, increased weight on arrival being protective for early mortality may have somewhat reflected this, as the distribution of weights was fairly tight and likely represented calves at a week of age or less. Although failure of passive transfer is a significant risk factor for mortality, the seasonal association we saw could reflect early life nutrition stress as opposed to seasonal variation in passive transfer. A further exploration of dairy farm of origin risk factors for veal calf mortality may serve to improve the productivity and welfare of dairy calves of both sexes.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».