Limitations of intensive meat rabbit production in North America: A review
Bibliographic record
Abstract
This paper documents underlying causes for the poor track record of the commercial meat rabbit industry in North America, relative to the success of several other species (cattle, swine, chickens and turkeys). For over half a century, efforts have been ongoing to develop a viable commercial meat rabbit industry. The progress has not been significant; rather, an accumulation of serious obstacles has targeted the species (e.g., high labor demand, no tradition of rabbit meat consumption, and nutritional limitations and behavioral constraints). Critical biological behaviors associated with the doe rabbit [e.g., short gestation and (or) underdeveloped neonates, cannibalism, territorialism, and pseudo-pregnancy] require that does be permanently placed into individual cages. These behaviors underpin the inability of management to offset labor by employing cost-effective automated feeding and management systems. As a consequence, labor costs per rabbit are high; rabbit meat is generally not competitive with more widely consumed meats. A proposed alternative solution is a redirected focus on rabbits as a “microlivestock” species — reared in small numbers as a family enterprise to enhance quality of life in rural and periurban areas, as well as in lesser developed countries — as opposed to further exploitation of the species as a commercial agricultural commodity. Key words: Rabbit, commercial, contemporary issues, industry, production
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".