Bibliographic record
Abstract
The role of the horse has been diverse in North America during the last century, including slaughtering horses for meat products intended for human consumption. Although horse meat is not culturally preferred in the diets of Americans and most Canadians, it is an acceptable meat source in the diet of Mexicans. The peak in the number of horses slaughtered in North America occurred in the 1980s, with as many as 350,000 horses per year. Over 90% of these horses were processed in the US, with the resultant meat products exported overseas. Public concern grew in the 1990s in the US on the alleged conditions of handling, transport, and slaughter of horses. Welfare concerns were focused on the utilization of the double-deck trailers, long transit duration without feed or water, use of an electric prod, and lack of provisions for injured, pregnant, old, or very young horses. This societal pressure promulgated the development of federal regulations on the safe and humane commercial transport of equines to slaughter. However, by the end of 2007, the last three processing plants in Texas and Illinois were closed due to state and federal laws. Closure of the processing plants precipitated a dramatic increase in the number of US-origin horses transported to slaughter plants in Canada and Mexico. With this shift in destination, welfare of the horses was compromised by increased duration of transport, inspection processes at the US borders, and the handling and stunning at slaughter facilities, especially in Mexico. Guidelines, regulations, and assessment tools for welfare of slaughter horses have been developed and implemented in Canada. The number of “unwanted” and neglected horses has increased in the US with the closure of the processing plants. The existing rescue and rehabilitation facilities in operation throughout the US do not meet the needs to care for these horses due to limited capacity and funding. Major challenges in the future sustainability of the industry will depend on several factors including the cultural preference for horse meat in the diet, the public's concern for the care and handling of horses during transport and slaughter, the impact of additional regulations, and the continued availability in the supply of horses, particularly those originating from the US.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".