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Enregistrement W4400614595 · doi:10.3168/jds.2024-25011

A randomized controlled trial evaluating the effect of providing a rest period during long-distance transportation of surplus dairy calves: Part I. Effect on health, growth, and activity

2024· article· en· W4400614595 sur OpenAlexafffundabout
H.M. Goetz, D.L. Renaud

Notice bibliographique

RevueJournal of Dairy Science · 2024
Typearticle
Langueen
DomaineVeterinary
ThématiqueAnimal health and immunology
Établissements canadiensUniversity of Guelph
Organismes subventionnairesAgriculture and Agri-Food CanadaMitacsDairy Farmers of Canada
Mots-clésLogistic regressionOddsMedicineAnimal scienceGompertz functionEnvironmental healthDemographyStatisticsMathematicsBiologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

The objective of this randomized controlled trial was to determine whether providing a rest period to surplus calves transported for 16 h reduced the effect of long-distance transportation on health, growth, and activity after arrival to a calf-raising facility. Between September 2022 to January 2023, 6- to 24-d-old surplus calves were randomly assigned to one of 2 treatment groups: (1) continuous transportation by road for 16 h (n = 86) or (2) 8 h of transport, an 8 h rest period, then a further 8 h of transport to a single commercial calf-raising facility in Ontario, Canada (n = 84). Calves that received the rest period were fed 2 L of milk replacer at the time of unloading for the rest period and again before reloading for the second leg of transportation. Calves were weighed before and after transportation and once weekly until they left the calf-raising facility at 11 wk after arrival. Additionally, fecal consistency and respiratory health were assessed immediately before and after transportation, as well as twice daily following arrival. The odds of abnormal fecal and respiratory scores were assessed with mixed effects logistic regression models, whereas Cox proportional hazards models were used to evaluate the hazard of treatment for diarrhea and respiratory disease. To evaluate weight loss during transportation, ADG, lying bouts, and steps, mixed linear regression models were used. Furthermore, mixed logistic regression models with repeated measures were built to assess associations with the odds of having a sunken flank and prolonged skin tent. There were no statistical differences in the odds of abnormal fecal or respiratory scores or the treatment for diarrhea between treatment groups; however, there was evidence that calves that received a rest period had a higher hazard of treatment for respiratory disease. Calves >10 d old had reduced odds of having an abnormal respiratory score and calves 13 to 24 d old had lower odds of arriving with a prolonged skin tent, lower odds of abnormal fecal score, and reduced hazard of treatment for diarrhea compared with calves that were 6 to 8 d old at transportation. During transportation, calves that received a rest period lost less weight than continuously transported calves. With respect to ADG over the 77-d study, an interaction between age at transport and treatment group was identified where calves aged 9 to 10 d old and receiving a rest period had higher ADG compared with calves that were 9 to 10 d old and not receiving a rest period. As for calf activity, calves receiving an rest period spent more time lying on the day after transportation compared with calves that were continuously transported. The results of this trial show few differences in the health and growth of calves having a rest period compared with those that underwent continuous transport. Furthermore, calves receiving a rest period may arrive more fatigued than calves that arrive at their destination in a shorter period of time overall. Transporting calves at older ages presents an opportunity to reduce the effect of long-distance transportation on subsequent health, growth, and activity.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: Essai randomisé
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,028

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,006
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0050,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,002
Communication savante0,0010,001
Science ouverte0,0020,001
Intégrité de la recherche0,0040,003
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

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.

Tête enseignante Opus0,038
Tête enseignante GPT0,383
Écart entre enseignants0,345 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeEssai randomisé
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2024
Routes d'admission3
Résumé présentoui

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