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Enregistrement W4252868101 · doi:10.1093/af/vfy004

Introduction

2018· article· en· W4252868101 sur OpenAlexaff
M.A. Steele

Notice bibliographique

RevueAnimal Frontiers · 2018
Typearticle
Langueen
Domaine
Thématique
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésComputational biologyBiology

Résumé

récupéré en direct d'OpenAlex

Antimicrobial resistance has emerged in the public eye as a serious threat for humanity and livestock agriculture. Based on World Health Organization reports, antimicrobial resistance is estimated to cost our global medical sector over 100 trillion dollars and cause 10 million human fatalities per year by 2050. These numbers become even more ominous when you add livestock production to the equation—a production sector that has traditionally relied on the use of antibiotics without clearly understanding the impact on humans or the environment. It is clear that antibiotics have been misused in livestock agriculture globally and already some countries, such as Denmark and the Netherlands, have banned their use for specific applications in livestock agriculture, such as growth promotion. Multinational companies are also now marketing antibiotic-free animal products. All of these factors compound the pressure on our international livestock sectors to reduce antibiotic use and discover alternatives. Included in this issue of Animal Frontiers are six articles showcasing different approaches to reducing antimicrobial use in livestock production. The first review, by “Dr. David Speksnijder” from Utrecht University (Netherlands), is entitled “Reducing antimicrobial use in farm animals: How to support behavioral change of veterinarians and farmers.” Using a social science lens, this article describes the underlying social mechanisms that motivate farmers and veterinarians to change often long-standing practices—an essential component in reducing the use of antibiotics on the farm level. From this unique social science perspective, we then move to the latest revolution in livestock production animal research: the search for antibiotic alternatives. Since our livestock production sector has relied on antibiotic use in routine management protocols, it would be short-sighted to think they can be removed without replacing them with sound alternatives. This search for such sound alternatives has opened the floodgates over the past decade, with public, academic, and industry researchers all investing heavily in alternative approaches. To make sense of the huge amount of data being generated, “Dr. Tim McAllister” from Agriculture Canada summarizes the latest developments in antibiotic alternatives in his article, “Challenges of a one-health approach to the development of alternatives to antibiotics.” Several novel approaches to antibiotic alternatives are gaining some momentum, including antimicrobial peptides, bacteriophages, and immunized products—all of which are reviewed by “Dr. Li” from Zhejiang University (China) and “Dr. Marquardt” from the University of Manitoba (Canada). The last article in this issue, “Bacterial resistance to antibiotic alternatives: A wolf in sheep’s clothing?”, by “Dr. Ben Willing” from the University of Alberta (Canada), provides an interesting perspective on how these alternatives to antibiotics may also cause bacterial resistance. The overall goal of this issue of Animal Frontiers is to provide insight into emerging concerns around antimicrobial resistance and offer viewpoints from some of the leading researchers in the field on how to reduce antibiotic use in livestock agriculture. It is clear that the pressure to reduce antibiotic use will only increase in the future, so the need to critically evaluate new strategies at the farm level is imperative. The initial research findings showcased in this issue are encouraging and suggest that it is possible to reduce use through the implementation of new approaches and technologies. Finding solutions to antimicrobial resistance in livestock agriculture will not come from one technology, but an integrated approach involving many different levels of farm management, public policy, and industry commitment. Are you ready for the challenge? Michael Steele is an assistant professor at the University of Alberta, NSERC Industrial Research Chair in Dairy Cattle Nutrition and President of the Canadian Society of Animal Science (CSAS). He completed his Ph.D. at the University of Guelph and worked for Nutreco Canada Agresearch for 2 years prior to returning to academia at the University of Alberta as an NSERC Industrial Research Chair. He was recently awarded the CSAS Young Scientist Award and the Lallemand Award for Excellence in Dairy Nutrition Research. His current research focuses on the mechanisms that control gastrointestinal health and development in cattle.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,739
Score d'incertitude au seuil0,875

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0020,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,2610,202

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 Opus0,008
Tête enseignante GPT0,233
Écart entre enseignants0,225 · 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.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentnon

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