Effects of pork differentiation strategies in Canada on pig performance and carcass characteristics
Bibliographic record
Abstract
Performance and quality traits were measured in carcasses from combinations of genotype, diet supplement, slaughter weight, and carcass chilling regime. Iberian-crossed pigs had lower live animal performance than Duroc and Lacombe. From 70 to 115 kg, Lacombe pigs grew slightly faster than Duroc. Duroc carcasses had a higher lean percentage, heavier ham and picnic primals, and lighter loins and bellies, compared with Lacombe and Iberian. Heavier carcasses had lower lean yield, except those from Iberian-crossed pigs, and bigger bellies. Meat from Duroc-crossed pigs was lighter in colour and higher in marbling, with intermediate values for fat hardness. Iberian carcasses displayed dark meat with intermediate marbling and the hardest fat. Meat from Lacombe pigs was the leanest although, like in all three breeds, marbling scores were higher in heavier carcasses. Generally, supplementing with canola and flax decreased and blast chilling increased fat hardness values. Dietary canola also seemed to affect meat colour traits, but these effects were not consistent among breeds and slaughter weights. The commercial combination of Duroc breed, control diet, and 115-kg slaughter weight showed a balance in terms of performance, carcass, and quality traits. Other combinations evaluated in this study showed potential to efficiently produce differentiated pork.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".