Review: Pork belly quality, bacon properties and recent consumer trends
Bibliographic record
Abstract
Soladoye, P. O., Shand, P. J., Aalhus, J. L., Gariépy, C. and Juárez, M. 2015. Review: Pork belly quality, bacon properties and recent consumer trends. Can. J. Anim. Sci. 95: 325–340. Several factors can affect pork belly quality and, subsequently, bacon quality. Going by the recent trends in the bacon market and bearing in mind the more choosy nature of the consuming populace, it is imperative to consider the factors that can affect or improve bacon quality, thereby sustaining the current market surge. In as much as both genetic and environmental factors have been identified as largely affecting muscle food quality, nutritional interventions also seem to be a very viable tool to improve the quality of meat and its products. Processing and storage methods can also affect bacon quality, including microbial quality, physicochemical attributes and palatability. Both objective and subjective measures have been explored in assessing belly quality, most of which use belly softness and fatty acid profile as yardsticks, whereas bacon quality has been widely assessed only subjectively in terms of fat quality and slice integrity. Although consumers’ and producers’ quality perceptions seem to be in conflict, it is the responsibility of all stakeholders in the bacon industries to come together in ensuring a balanced approach to satisfy both parties along the production chain.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".