RESEARCH OF ESCHERICHIA COLI SEROTYPE O157 IN BEEF CARCASSES AND FAECAL MATERIAL
Bibliographic record
Abstract
The aim of this paper is to describe a methodology applicable to the isolation of viable Escherichia coli O157 from beef carcasses and faecal samples and specifically to overcome problems of background flora outgrowth, which can reduce sensitivity of the test. 100 samples were collected in a slaughterhouse of province of Ravenna from December 2001 to January 2003, namely carcass samples and rectum content of 50 animals 2 x 2.5 cm fragments (tick <0.5 cm) were excised aseptically from rump, flank, brisket and neck before chilling. Faeces were taken with a sterile spoon after incision of rectum in the tripery. The entire pool of tissue fragments from each carcass (equivalent to 20 cm2) and 25 grams of faecal material were used to isolate Escherichia coli serotype O157 with the Laboratory Procedures MFLP-80 and MFLP-90 (Directorate’s-Health Canada’s). Faeces from cattle contain an huge number of bacteria, other then E.coli, that can survive/multiply in presence of Tryptone Soya Broth added with bile salts and novobiocin (20 mg/lt). Immunomagnetic enrichment procedure has proved useful to capture specifically E. coli O157, but other organisms were found in a great number, thus reducing the sensitivity of the test, even because diffusion of the fluorescent pigments in the Phenol Red Sorbitol Agar with MUG made it impossible to detect β-glucuronidase-negative colonies. HC agar with MUG proved to be more selective so decreasing background bacteria outgrowth. Besides the use of sterile swab instead of glass spatula and the technique of streaking helped very much in detection of isolated colonies on both agar media.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".