Meat composition and quality of young growing Belgian Blue bulls offered a fattening diet with selenium enriched cereals
Bibliographic record
Abstract
Mehdi, Y., Clinquart, A., Hornick, J.-L., Cabaraux, J.-F., Istasse, L. and Dufrasne, I. 2015. Meat composition and quality of young growing Belgian Blue bulls offered a fattening diet with selenium enriched cereals. Can. J. Anim. Sci. 95: 465–473. The objective of this study was to evaluate the effects of selenium (Se) enrichment of cereals on the performance of Belgian Blue bulls, meat quality and chemical composition. Twenty-three bulls were used in the present study. Twelve bulls were offered a control diet containing Se at a basal concentration of 58 µg kg−1 of dry matter (DM) and the other 11 bulls were given a diet containing 173 µg kg−1 DM of Se by means of Se-enriched spelt and barley. The Se enrichment of the diet did not affect the growth performance, the slaughter data or meat quality (P > 0.05). There were no effects of Se on tenderness, oxidative rancidity and water losses. However, there were some effects of Se enrichment on the meat chemical composition. The ether extract was decreased from 2.1 to 1.7% DM (P<0.05). There was also significant Se enrichment (P<0.001) in the longissimus thoracis muscle (177 vs. 477 ng g−1 DM) and organs: liver (474 vs. 1126 ng g−1 DM) and kidney (4956 vs. 5655 ng g−1 DM), Under such conditions, the human consumption of a piece of such meat or liver can provide a large part of the recommended daily Se intake, estimated between 30 and 57%.
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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) | 0.001 | 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".