Monitoring toxigenic <i>Microcystis</i> strains in the Missisquoi bay, Quebec, by PCR targeting multiple toxic gene loci
Bibliographic record
Abstract
The increasing incidence of mixed assemblages of toxic and nontoxic cyanobacterial blooms in Quebec's freshwater bodies over the last decade, coupled with inherent inadequacies of current monitoring approaches, warrants development of sensitive and reliable tools for assessing the toxigenic potential of these water blooms. In this study, we applied three independent polymerase chain reaction (PCR) assays that simultaneously target the microcystin synthetase (mcy) genes A, E, and G to rapidly and reliably detect and quantify potentially toxic Microcystis genotypes in the Missisquoi bay, Quebec, Canada. Linear regressions of quantitative PCR threshold cycles (Ct ) against the logarithm of their respective Microcystis cell number equivalents resulted in highly significant linear curves with coefficients of determination (R(2) ) greater than 0.99 (p < 0.0001, n = 6) and reaction efficiencies of 91.0, 95.8, and 92.7%, respectively, for the mcyA, mcyE, and mcyG-based quantitative real-time PCR (qPCR) assays. The three assays successfully estimated potential microcystin-producing Microcystis genotypes from all field samples. The proportions of MicrocystismcyA, mcyE, and mcyG genotypes to total Microcystis cell counts showed substantial spatial variability ranging between 1.7-21.6%, 1.9-11.2%, and 2.2-22.6%, respectively. Correlation of microscopically determined total Microcystis counts to qPCR-based MicrocystismcyA, mcyE, or mcyG cell number equivalents resulted in highly significant associations with R(2) > 0.90. Thus, PCR-based assays targeting the mcyA, mcyG, and/or mcyE genes can serve as powerful screening tools for rapid and sensitive estimation of microcystin-producing Microcystis genotypes in freshwater water bodies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".