PSX-22 A survey of North American yak owners with a focus on meat production.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".