The economics of hog carcass grading in Ontario
Bibliographic record
Abstract
This thesis estimates the benefits of hog carcass quality from 1969-2000 and the potential benefits of implementing a new grading technology to the Ontario pork industry. Using the economic surplus approach, the total benefits to hog processor and hog grower are estimated from 1969-2000, along with the distribution of historical benefits between processor and grower. The AutoFOM grading system is the proposed grading technology that can address several of the concerns with Ontario's current grading system and further improve the quality of the hog carcasses currently delivered to market in Ontario. Estimated benefits from the improvements in carcass quality during 1969-2000 are considerable for both hog processors and hog growers. Potential benefits from the AutoFOM system demonstrate the feasibility of Ontario adopting the AutoFOM grading technology. Benefits of AutoFOM are estimated for processors and growers. The results show that adopting AutoFOM into the Ontario pork industry could return benefits to both the processors and growers.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".