qPCR-based prediction of low-level microcystin-LR using mcyE and passive sampling across multiple lakes and years
Bibliographic record
Abstract
Microcystin-LR (MC-LR) is a cyanobacterial hepatotoxin that poses health risks even at low concentrations. Because quantitative analysis of MC-LR is costly and time-consuming, water managers rely on early warning tools to determine when confirmatory testing is warranted. Quantitative PCR (qPCR) targeting the mcy genes has emerged as one such tool, but its reliability across lakes and seasons — particularly at low toxin concentrations — remains unclear. In this study, we used passive sampling to detect low concentrations (< 1 µg L −1 ) of MC-LR and paired this with qPCR monitoring of mcyE to assess whether mcyE alone can serve as a reliable indicator of low-level MC-LR presence over three years across ten lakes (total of n = 893 distinct samples). We developed location- and year-specific hierarchical Bayesian models to estimate the probability of MC-LR detection from mcyE concentrations. We also included environmental covariates to determine if their inclusion improved model performance. Although mcyE was the strongest overall predictor, its relationship with MC-LR varied substantially by location and year, and these hierarchical models were essential in capturing this variability. These findings highlight the promise of mcyE -based early warning systems for low-concentrations of MC-LR but emphasize that interpretation must be tailored to local ecological and seasonal conditions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".