Freezing Inactivation of <i>Escherichia Coli</i> and <i>Enterococcus Faecalis</i> in Water: Response of Different Strains
Bibliographic record
Abstract
The effect of freezing temperature (-7, -15, -30 and -80 degrees C), number of freeze/thaw cycles (1 to 5 cycles) and sample volume (100 mL and 100 microL) on the viability of a pathogenic and an opportunistically pathogenic Escherichia coli, a vancomycin-resistant and a vancomycin-sensitive Enterococcus faecalis were examined. About 3.3 to 4.3 and 1.5 to 2.4 log reduction in cell density were observed in E. coli and E. faecalis, respectively, in the 100 mL samples frozen at -30 degrees C or warmer. Freezing at -80 degrees C was the least effective in killing the microbes, on average the log reduction at -80 degrees C was approximately 1.0 to 1.5 units less than those achieved at the three warmer temperatures. Based on statistical analysis, cell inactivation levels achieved at -7, -15, or -30 degrees C were not significantly different (P-value = 0.1648). There were no statistical difference in terms of log reduction obtained under all experimental conditions for the two E. coli strains (P-value = 0.46) and the two E. faecalis strains (P-value = 0.10). The number of freezing/thaw cycles and sample volume, however, profoundly affected inactivation capacity of freezing. Freezing could be an effective method for further reduction of E. coli and Enterococcus in municipal wastewater/sludge.
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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) | 0.001 | 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".