An innovative passive sampling approach for the detection of cyanobacterial gene targets in freshwater sources
Bibliographic record
Abstract
Cyanotoxins pose significant human health risks, but traditional monitoring approaches can be expensive, time consuming, and require analytical equipment or expertise that may not be readily available. Quantitative polymerase chain reaction (qPCR) is becoming an increasingly common monitoring strategy as detection of the genes responsible for cyanotoxin synthesis can be used as an early warning signal. Here we tested passive sampling of cyanobacterial DNA as an alternative to grab sampling in a freshwater drinking supply lake with a known history of microcystin-LR. DNA extracted from grab and passive samples was analyzed via a multiplex qPCR assay that included gene targets for four common cyanotoxins. Passive samples captured similar trends in total cyanobacteria and the mcyE/ndaF gene responsible for microcystin production when compared to traditional grab samples. Passive samples also detected genes associated with the production of cylindrospermopsin and saxitoxin that were not detected in grab samples. This sampling approach proved a viable alternative to grab sampling when used as an early warning monitoring tool. In addition to the logistical benefits of passive sampling, the detection of gene targets not detected by grab samples indicates that passive sampling may allow for a more complete profile of potential cyanotoxin risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".