PSII-8 Defining pig sort loss with a simulation of various marketing options of pigs with the assumption that marketing cuts improve variation in carcass weight and leanness
Bibliographic record
Abstract
Abstract The study offers clarification on pig sort loss and associated marketing strategies using a simulated pig marketing modeling system. The objective was to investigate the economic variability associated with marketing strategies using the simulated pig marketing models. Typically, individual pigs are assessed by measuring carcass weight and predicted leanness, which is then incorporated into a two-factor grid for producer payment, providing incentives for producers who consistently produce desirable carcasses and discounts for producers who produce inconsistent or undesirable carcasses. The simulation considered six producers with the presumption that each had a maximum capacity for 4,800 grow-finish pigs, in order to imitate commercial finishing barns with 48 pens of roughly 100 pigs per pen. The simulation dataset was created using a random number generator with the inverse of the cumulative normal distribution function on Microsoft Excel (Microsoft Inc., USA) with a targeted carcass weight (102.86 kg) and average predicted lean (60%) based on industry averages and previous research studies. Under the assumption that variability in carcass weight and predicted leanness decreased with the addition of each marketing cut, the simulation incorporated a standard deviation reduction of 20% per increase of one marketing cut for both carcass weight and predicted leanness of the population of pigs marketed on a given day. Consequently, there was an increase in profitability; as well as, a decrease in pig sort loss (defined with both carcass weight and predicted leanness) with each marketing cut, but these profitability improvements diminished (as a percentage improvement) with each additional marketing cut. Finally, this simulation provides an appropriate framework and the necessary equations to allow repetition of the different parameters and marketing grid specifically related to an individual producer and processing facility. Thus, helping the industry gain a better understanding of how market cuts can decrease variation and consequently improve profitability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".