Pricing Hogs Using a Seasonal Varying Percentage of the Pork Cutout Value
Bibliographic record
Abstract
Pricing hogs using a percentage of the pork cutout value is intuitively attractive. Producers get paid directly and consistently based on the value of pork cuts. Packers receive a consistent share of the cutout value to add to the drop value they capture to provide a gross margin from which to pay all other costs. Using a percentage of the cutout provides an incentive for packers to always maximize the carcass value―the higher the cutout, the more the packer makes. However, using a constant percentage of the cutout value to price hogs, which is how most, if not all, pork-price formulas are currently constructed, provides packers less incentive to work extra shifts to handle the normal fourth quarter and January surge in market hog slaughter numbers. This issue will only continue to grow if a larger and larger share of swine or pork market formulas use a flat percentage of the cutout to price hogs. This article identifies a solution to allow the cutout value percentage used in formulas to vary during the year. The varying percentage peaks in the summer and bottoms in the late-fall and early- winter. This would help ensure packers receive higher gross margins during the fourth quarter and January to compensate for higher costs. Producers and packers could utilize historical long-run patterns in negotiations when establishing formula percentages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".