The influence of narasin level, type of feed, and gender on the palatability attributes and cooking properties of pork
Bibliographic record
Abstract
A total of 256 pork chops were obtained from pigs of different genders (128 barrows and 128 gilts) produced at two different geographical locations (64 barrows and 64 gilts produced in Alberta and in Ontario). Pigs received two different types of feed (32 barrows and 32 gilts received either mash or pellets at each location) with or without narasin treatment (16 barrows and 16 gilts received either 0 or 15 ppm of narasin, within each geographical location and feed type ). Chops were evaluated for the influence of these production factors on palatability attributes (initial and overall tenderness, amount of perceptible connective tissue, juiciness, flavor intensity and desirability, and overall palatability) and cooking properties (thaw-drip losses, total cooking losses and cooking times). Results clearly demonstrated 15 ppm of narasin could be incorporated into the diet of growing/finishing pigs without influencing the palatability attributes (initial and overall tenderness, amount of perceptible connective tissue, juiciness, flavor intensity, flavor desirability, and overall palatability) or cooking properties of the final product. Neither gender nor feed type exerted influences of practical importance on palatability attributes or cooking properties. Key words: Pork, narasin, ionophores, feed type, gender, palatability, cooking properties
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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 | 0.000 | 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) | 0.002 | 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".