Notice bibliographique
Résumé
Research’s aim: To find out which are the main risk factors predisposing dog in obesity development as well as the investigation of the prevalence of dog obesity in X small animal clinic. The research was carried out in X clinic in Cyprus over a period of 4 months. The survey included 26 multiple choice questions and a single open question. 249 dogs took part in the survey including pure breed dogs as well as crossbred ones. A BCS scale (five point system) was attached to each questionnaire in order to assist the owners of the dogs in body condition scoring of their dogs. As well as dog owners were asked to answer questions regarding dogs age, gender, neutering status, diet, activity level as well as concurrent diseases related to obesity if presented and eventually owners of the dogs were questioned whether they are informed about treatment and preventive measures against obesity in dog. Eventually obese dogs were isolated from the rest of the sample and data was analyzed. Statistical calculations showed that older dogs are more prone to obesity, 75 perc. of obese dogs are older than 5 years old. Most obese dogs belong to these breeds: Beagles, Labrador Retrievers, Pekingese, Dachshund, as well as crossbreed dogs. Obesity is related to dog owner’s attitude while 69,83 perc. of dog owners who own obese dogs aren’t aware of their dog’s weight. Furthermore 62,50 perc. of owners who keep more than one dog in the household indicate a lack of attention to each of their dog’s health and weight status as well as 57,45 perc. of owners are not aware of preventive measures for obesity. Dogs whose owners provide them dry food once a day or constantly, do not estimate their feed amount, giving table scraps and treats are more likely to be obese. Limited mobility within their permanent living environment, limited physical activity- not walking at all or walking once or twice a day till 15 min. predispose dogs to obesity development. Obese dogs are more likely to suffer from osteoarticular disorders, heart problems, respiratory problems or skin disease. Obese dogs owners do not have sufficient knowledge regarding treatment of obesity. The main preventive measures are daily exercise and providing the dog with correct food amount.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 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 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 ».