Use of in vivophycocyanin fluorescence to monitor potential microcystin-producing cyanobacterial biovolume in a drinkingwater source
Bibliographic record
Abstract
The source water of a drinking water treatment plant prone to blooms, dominated by potential microcystin-producing cyanobacteria, was monitored for two seasons in 2007-2008. In the 2008 season, the median value for potential microcystin-producing cyanobacterial biovolume was 87% of the total phytoplankton biovolume in the untreated water of the plant. Depth profiles taken above the plant's intake identified three sampling days at high risk for the contamination of the plant's raw water with potentially toxic cyanobacteria. Chlorophyceae and Bacillariophyceae caused false positive values to be generated by the phycocyanin probe when cyanobacteria represented a small fraction of the total phytoplanktonic biovolume present. However, there was little interference with the phycocyanin probe readings by other algal species when potential microcystin-producing cyanobacteria dominated the phytoplankton of the plant's untreated water. A two-tiered method for source water monitoring, using in vivo phycocyanin fluorescence, is proposed based on (1) a significant relationship between in vivo phycocyanin fluorescence and cyanobacterial biovolume and (2) the calculated maximum potential microcystin concentration produced by dominant Microcystis sp. biovolume. This method monitors locally-generated threshold values for cyanobacterial biovolume and microcystin concentrations using in vivo phycocyanin fluorescence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".