Occurrence of microcystins, anabaenopeptins and other cyanotoxins in fish from a freshwater wildlife reserve impacted by harmful cyanobacterial blooms
Bibliographic record
Abstract
Harmful algal blooms of cyanobacteria (CyanoHABs) can lead to the release of potent toxins that can seriously affect ecosystem integrity. Some freshwater watersheds are particularly at risk considering the threats to already imperiled wildlife. The consumption of tainted drinking water and contaminated food also raises concerns for human health. In the present study, a pilot survey was conducted in the riverine ecosystem of the Pike River Ecological Reserve (QC, Canada) near Missisquoi Bay, Lake Champlain. We examined the occurrence of multiclass cyanotoxins including 12 microcystins, anatoxins, cylindrospermopsin (CYN), anabaenopeptins (AP-A, AP-B), and cyanopeptolin-A in surface waters and wild-caught fish during the summer 2018. Out of the 18 targeted cyanotoxins, 14 were detected in bloom-impacted surface water samples; toxins peaked during early-mid September with the highest concentrations for MC-LR (3.8 μg L−1) and MC-RR (2.9 μg L−1). Among the 71 field-collected fish from 10 species, 30% had positive detections to at least one cyanotoxin. In positive samples, concentration ranges in fish muscle were as follows for summed microcystins (∑MCs: 0.16–9.2 μg kg−1), CYN (46–75 μg kg−1), AP-A (1.1–5.4 μg kg−1), and AP-B (0.12–5.0 μg kg−1). To the best of our knowledge, this is one the first reports of anabaenopeptins occurrence in wildlife. The maximum ∑MCs in fish was 1.15-fold higher than the World Health Organization (WHO) daily intake recommendation for adults and nearly equated the derived value for young children. The concentration of CYN was also about 3-fold higher than the limit derived from the human health guideline values.
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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 | 0.000 |
| 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 | 0.000 | 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".