Technical Note: A characterization of Argentinian pork fabrication techniques
Bibliographic record
Abstract
A main concern during the rapid growth of the Argentinian pork industry that has not been addressed is inconsistency and unknowns in carcass cutting techniques and specifications. The objectives of this study were to characterize pork carcass fabrication techniques in the Argentinian commercial pork industry. Pigs (n = 100) from 4 Argentinian pork suppliers were used. Pigs were slaughtered at a commercial pork processing facility and air chilled at 4°C for 24 to 48 h. Left carcass sides were fabricated into 5 primals according to specifications used in the commercial Argentinian pork industry: jamón, carre, pecho con manta, bondiola, and paleta. Weights of primals were recorded immediately after fabrication. Primals were further fabricated into subprimal pieces according to standard procedures of the commercial pork processing facility. Primal and subprimal weights were reported as raw weights and as a percentage of total HCW (head on). Weights of primals and subprimals were characterized as descriptive data and then compared among suppliers. When expressed as a percentage of HCW (head on), the jamón primal was 26.99 ± 0.12% of HCW, the carre was 10.70 ± 0.12% of HCW, the pecho con manta primal was 17.21 ± 0.12% of HCW, the bondiola primal was 6.75 ± 0.06% of HCW, and paleta was 15.79 ± 0.10% of HCW. Overall, the understanding of commercial cutting techniques will allow the Argentinian pork industry to become more consistent, and comparing cuts of primals and subprimals with North American Meat Processors (NAMP) specifications may allow for a greater understanding of the Argentinian pork industry worldwide.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".