Use of <i>Bacillus subtilis</i> spores as model micro-organisms for ozonation of <i>Cryptosporidium parvum</i> in drinking water treatment
Bibliographic record
Abstract
Spores of the aerobic bacterium Bacillus subtilis are potential models for evaluation of Cryptosporidium parvum inactivation by ozone processes in drinking water treatment. The kinetics of inactivation by ozone of prepared Bacillus subtilis ATCC 6633 spores were measured in a series of batch reactor experiments at temperatures of 3, 12, and 22°C and at a pH of 6 and 8. Spore inactivation curves were characterized by a pronounced lag phase at low ozone exposures followed by a logarithmic inactivation rate and, finally, by a tailing region at higher ozone exposures. A two-part kinetic model consisting of a multitarget term and a first-order term provided a good fit to observed inactivation at each temperature. Water pH had a statistically significant but limited effect on spore inactivation. When spore inactivation was compared to C. parvum oocyst inactivation predicted by a previously published kinetic model, substantial differences were observed. These differences may make it difficult to use this particular strain and preparation of spores to reliably predict C. parvum oocyst inactivation from the outcomes of B. subtilis seeding studies. Bacillus subtilis spores may otherwise serve as indicators of the hydrodynamic efficiency of ozone contactors. Key words: ozone, inactivation, Bacillus subtilis spores, Cryptosporidium parvum oocysts, drinking water, kinetics, modeling.
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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 | 0.000 | 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) | 0.000 | 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 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".