Use of environmental DNA to investigate the distribution of microcystin-producing Microcystis in Eastern Ontario
Bibliographic record
Abstract
The emergence and persistence of algae blooms, comprising multiple toxicity-producing cyanobacteria genera, pose great threats to aquatic environments, to many native species, and to human health. In Ontario, cyanobacteria blooms were reported from across the province spanning 2009 to 2014, mainly occurring in Eastern Ontario as well as the southeastern part of Northern Ontario. Eastern Ontario is predicted to face increasing risk of cyanobacteria blooms in the future. microcystin-producing Microcystis is one of the dominant toxin-producing cyanobacteria genera, which presents many of the most significant challenges for water quality and public health. In this study, I develop and test a quantitative real-time polymerase chain reaction (qPCR) and environmental DNA (eDNA) approach to assess the distribution of microcystin-producing Microcystis in Eastern Ontario and estimate potential toxicity in 43 water bodies sampled in the summer of 2017. The qPCR assay was used to detect the Microcystin synthetase gene E (mcyE gene) in water samples. The limit of detection of qPCR was 3.06E+05 copies/L for mcyE. microcystin-producing Microcystis was detected in 28 out of total 43 water bodies. My research proved that a qPCR assay developed to target Microcystis gene fragments was specific and efficient for rapid detection of Microcystis and for diagnosing its toxicity.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".