Chlorination for degrading saxitoxins (paralytic shellfish poisons) in water
Bibliographic record
Abstract
Chlorination was investigated as a treatment option for degrading and thus removing saxitoxins (paralytic shellfish poisons, PSPs) produced by cyanobacteria (blue-green algae) from water. It was found to be effective with the order of ease of degradation of the saxitoxins being GTX5 (B1) approximately dcSTX > STX > GTX3 approximately C2 > C1 > GTX2. However the effectiveness of chlorine was pH dependent. Degradation as a function of pH was not linear with the degree of degradation increasing rapidly at around pH 7.5. At pH 9 > 90% removal was possible provided a residual of 0.5 mg l(-1) free chlorine was present after 30 min contact time. The more effective degradation at higher pH was unexpected as chlorine is known to be a weaker oxidant under these conditions. The more effective degradation, then, must be due to the toxins, which are ionisable molecules, being present in a form at higher pH which is more susceptible to oxidation. The feasibility of using chlorine to remove saxitoxins during water treatment will therefore depend strongly on the pH of the water being chlorinated. Degradation may be improved by pH adjustment but may not be a practical solution. Although saxitoxins were degraded in that the parent compounds were not detected by chemical analysis, there is no indication as to the nature of the degradation products. However, acute toxicity as determined by the mouse bioassay was eliminated.
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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) | 0.000 | 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) | 0.001 | 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".