BACTERIAL PROFILE OF RETAIL RABBIT CARCASSES MARKETED IN BENI-SUEF PROVINCE, EGYPT
Bibliographic record
Abstract
The current study aimed to evaluate the bacteriological status of retail rabbit carcasses marketed in Beni-Suef province, Egypt. For such aim, a total of 25 fresh rabbit carcasses were randomly collected from different rabbit markets in Beni-Suef during 2015. The collected samples were subjected to determination of aerobic plate count (APC) at 35 °C, and most probable number (MPN) of coliforms, faecal coliforms and E. coli, in addition to isolation and identification of E. coli (true faecal type), Salmonella spp.and Yersinia enterocolitica. The obtained results revealed that 32, 64, 72 and 52 % of examined rabbit meat samples from shoulder, loin, rib and thigh regions, respectively, exceeded the acceptable limits recommended by Egyptianstandardsfor APC (105 CFU/g flesh). While none of the examined samples exceeded the international standards (107 CFU/g) stated by the International Commission on Microbiological Specification for Foods (ICMSF). Regarding the pathogenic microorganisms, it was found that 11 (44 %), 8 (32 %), 15 (60 %) and 10 (40 %) out of 25 rabbit cuts contained E. coli biotype I in shoulder, loin, rib and thigh regions, respectively. However, 3 (12%), 3 (12%), 3 (12%) and 2 (8%) samples containedSalmonella spp., and 3 (12%), 3 (12%), 6 (24%) and 7 (28%) containedYersinia enterocolitica, respectively. The public health significance of isolated pathogens and their sources of contamination were discussed throughout the study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".