Reduction of microbial contamination of goat meat using dietary brown seaweed (<i>Ascophyllum nodosum</i>) supplementation and chlorinated wash
Bibliographic record
Abstract
This experiment was conducted to determine the effects of brown seaweed extract (Ascophyllum nodosum) supplementation and chlorinated skin wash on skin and carcass microbial contamination in goats. In a completely randomized design with split-plot, 32 Boer × Spanish bucks were fed a diet containing alfalfa pellets (60%) and Tasco feed supplement (40%) with (four pens) or without (four pens) seaweed extract for 14 d. Goats were processed in two batches. Two bucks from each pen were spray washed with chlorinated water (50 mg L−1) after stunning and bleeding, and the other two bucks were processed as unwashed controls. Skin swab samples were obtained from the hind legs (5 cm × 5 cm) prior to overnight holding, after holding, and after spray washing. Immediately after skinning and evisceration, carcass swab samples were taken to assess contamination levels. The effect of spray wash on aerobic plate counts on skin (3.65 vs. 4.30 log10 CFU cm−2) was significant (P < 0.05). Also, the goats subjected to seaweed extract dietary treatment plus spray wash had the lowest skin Escherichia coli counts. Seaweed extract supplementation before slaughtering, combined with chlorinated spray wash during processing, can be used as a viable decontamination strategy in goat processing.
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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".