Evaluation of Chromogenic Enzyme Substrate Mediums, Chromocult Coliform Agar(CCA) and XM-G, by Detection of Freeze-, Heat-, High-Pressure-Injured Coliforms, and Coliforms in Food Samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The two chromogenic enzyme substrate mediums of chromocult coliform agar (CCA) and XM-G were applied to detect freeze-, heat-, and high-pressure-injured coliforms (Enterobacter aerogenes, Enterobacter cloacae, Escherichia coli, Klebsiella ozaenae, and Klebsiella pneumoniae) as well as coliforms in various food items. Their detection abilities were then compared with the following three conventional media: tryptic soy agar (TSA), violet red bile agar (VRBA), and VRBA/TSA. The enumerated results of injured coliforms showed that the ability to detect injured cells was in the following descending order: non-selective medium TSA>VRBA/TSA>XMG≥CCA≫VRBA. The recovery rate of injured coliforms, when compared with selective agars, was higher in CCA and XM-G than in VRBA. Investigation of the total coliform counts from 100 food samples showed that the enumeration results of the three selected media (CCA, XM-G, and VRBA) were quite similar. The correlation coefficients were 0.89 for CCA vs. VRBA, 0.91 for XM-G vs. VRBA, and 0.91 for CCA vs. XM-G; indicating that CCA and XM-G are recommendable and can substitute for the conventional selective medium VRBA. In addition to the advantage of simultaneous detection of coliforms and Escherichia coli by CCA and XM-G, their superiority in detecting injured coliforms reveals that these two methods were highly effective and suitable to monitor total coliforms and E. coli including injured cells in food samples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it