PSX-22 A survey of North American yak owners with a focus on meat production.
Notice bibliographique
Résumé
Abstract Yaks can be found across much of North America, yet, little information is available on yak production outside the Tibetan Plateau region. Using an online survey software (Qualtrics XM), yak producers/owners completed a questionnaire to provide insight on management practices. A QR code was displayed at the 2022 Northwestern Stock Show and a link was shared with the US Yak and IYAK association members. A total of 70 responses were recorded with 48 surveys being utilized in the analysis. ProcFreq of SAS 9.4 was used to summarize responses. Data were further filtered with those indicating “No” for raising animals for meat excluded in additional analyses. Participants indicated raising yaks across a wide range of the United States with 18 states from Washington to New Jersey, and Canada to Texas. The mean number of breeding females, males and meat animals were 21, 3 and 13, respectively. An educational opportunity was identified for record keeping as 34% of respondents indicated they did not maintain animal management records and less than 11% routinely weighed animals. Only 45% maintained breeding or pedigree records while slightly more than half maintained health and input cost records. Seventy-seven percent indicated they raised yaks for meat with the average number raised per operation for meat being six animals. When asked why respondents raised yaks in which multiple responses were allowed, more than 80% indicated for meat production, 54% for fiber production, 52% were raising them for breeding stock, 52% as a hobby, and to a lesser degree for trekking, rehabilitation programs for veterans, milk, agritourism and land regeneration. Sixty-five percent of those raising yaks for meat indicated fewer animals were marketed than desired. The major limitation for not meeting market goals was the limit of animals that could be raised on the farm/ranch (62.2%) followed by the lack of USDA slaughter capacity (48.7%). Other reasons included lack of further processing (ie. jerky), variability in available animals, insufficient time/labor, lack of market opportunity and lack of animals available to purchase for meat production. Almost 49% indicated they purchased animals for meat production with the mean number purchased being less than five animals. The majority, 73%, indicated yaks were raised in a strictly grass/forage-based system while 19% indicated a mostly grass/forage-based system with limited grain/protein supplementation. Participants were asked to share prices received for various meat products. At the time of the survey, the average price indicated for ground yak was $22.53/kg while roast and steaks were $27.50/kg and $38.28/kg, respectively. Yak production in North America is widespread across a variety of climates for the production of meat, fiber, breeding stock and pleasure. Additional research is warranted to assess their potential as a source of domesticated lean red meat.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».