Experimental study on cryptosporidium inactivation in drinking water by chlorine-based disinfectants
Bibliographic record
Abstract
In order to find chlorine-based disinfectants' inactivation effect on cryptosporidium in drinking water,factors influencing inactivation efficiency such as the dosage of chlorine and chlorine dioxide,contact time,turbidity,pH value and temperature were investigated. It was found that optimal cryptosporidim inactivation efficacy of chlorine-based disinfectants was obtained at 360 min with chlorine dosage of 6. 5 mg / L and at 120 min with chlorine dioxide dosage of 3. 0 mg / L,respectively. Cryptosporidium inactivation ratio of chlorine reduced with higher turbidity; cryptosporidium inactivation ratio of chlorine dioxide was almost stable when turbidity increased to 5. 0 and 10. 0 ntu. The fluctuating of pH value had little influence on inactivation by chlorine; optimal inactivation efficiency of chlorine dioxide was obtained at pH value of 6 ～ 7. In the temperature range of 5 ～ 35 ℃,inactivation ratio was enhanced with the increase of water temperature.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 teacher head, 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".