Alternative slaughter procedures: on-farm slaughter and transport system for broilers
Bibliographic record
Abstract
This paper focuses on "alternative methods for initial broiler processing" and exploration of alternative processing including slaughter at the farm immediately after catching. On-farm slaughter and transport (FSaT) is envisioned as a mobile unit that stuns, slaughters, and shackles the broiler carcasses at the farm. A separate trailer-unit then transports the shackled broiler carcasses to the processing plant. Once at the processing plant carcasses are mechanically transferred into plant shackle lines and moved into processing. The hypothesis is that the FSaT approach will dramatically improve overall bird welfare and well-being by reducing live handling and eliminating live transport from the farm to the processing plant. In addition, ancillary impacts could include: improving yield efficiencies by eliminating dead on arrivals, potentially reducing water and energy consumption, reducing labor requirements at the processing plant with the elimination of live rehang, and offering an economically sustainable alternative. The FSaT approach represents a radical change from traditional processing, and its effects on poultry processing need to be evaluated. This paper presents results of experiments conducted at a commercial poultry processor to evaluate feather picking efficiency, carcass bacteriological loading, and meat quality for delayed processed carcasses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".