Application of in vivo measurements for the management of cyanobacteria breakthrough into drinking water treatment plants
Bibliographic record
Abstract
The increasing presence of potentially toxic cyanobacterial blooms in drinking water sources and within drinking water treatment plants (DWTPs) has been reported worldwide. The objectives of this study are to validate the application of in vivo probes for the detection and management of cyanobacteria breakthrough inside DWTPs, and to verify the possibility of treatment adjustment based on intensive real-time monitoring. In vivo phycocyanin YSI probes were used to monitor the fate of cyanobacteria in raw water, clarified water, filtered water, and chlorinated water in a full scale DWTP. Simultaneous samples were also taken for microscopic enumeration. The in vivo probe was successfully used to detect the incoming densities of high cyanobacterial cell number into the clarification process and their breakthrough into the filtered water. In vivo probes were used to trace the increase in floating cells over the clarifier, a robust sign of malfunction of the coagulation-sedimentation process. Pre-emptive treatment adjustments, based on in vivo probe monitoring, resulted in successful removal of cyanobacterial cells. The field results on validation of the probes with cyanobacterial bloom samples showed that the probe responses are highly linear and can be used to trigger alerts to take action.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".