Monitoring of potentially toxic cyanobacteria using an online multi-probe in drinking water sources
Bibliographic record
Abstract
Toxic cyanobacteria threaten the water quality of drinking water sources across the globe. Two such water bodies in Canada (a reservoir on the Yamaska River and a bay of Lake Champlain in Québec) were monitored using a YSI 6600 V2-4 (YSI, Yellow Springs, Ohio, USA) submersible multi-probe measuring in vivo phycocyanin (PC) and chlorophyll-a (Chl-a) fluorescence, pH, dissolved oxygen, conductivity, temperature, and turbidity in parallel. The linearity of the in vivo fluorescence PC and Chl-a probe measurements were validated in the laboratory with Microcystis aeruginosa (r(2) = 0.96 and r(2) = 0.82 respectively). Under environmental conditions, in vivo PC fluorescence was strongly correlated with extracted PC (r = 0.79) while in vivo Chl-a fluorescence had a weaker relationship with extracted Chl-a (r = 0.23). Multiple regression analysis revealed significant correlations between extracted Chl-a, extracted PC and cyanobacterial biovolume and in vivo fluorescence parameters measured by the sensors (i.e. turbidity and pH). This information will help water authorities select the in vivo parameters that are the most useful indicators for monitoring cyanobacteria. Despite highly toxic cyanobacterial bloom development 10 m from the drinking water treatment plant's (DWTP) intake on several sampling dates, low in vivo PC fluorescence, cyanobacterial biovolume, and microcystin concentrations were detected in the plant's untreated water. The reservoir's hydrodynamics appear to have prevented the transport of toxins and cells into the DWTP which would have deteriorated the water quality. The multi-probe readings and toxin analyses provided critical evidence that the DWTP's untreated water was unaffected by the toxic cyanobacterial blooms present in its source water.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".