Amplicon-based and metagenomic approaches provide insights into toxigenic potential in understudied Atlantic Canadian lakes
Bibliographic record
Abstract
Cyanobacterial blooms and their toxigenic potential threaten freshwater resources worldwide. In Atlantic Canada, despite an increase of cyanobacterial blooms in the last decade, little is known about the toxigenic potential and the taxonomic affiliation of bloom-forming cyanobacteria. In this study, we employed polymerase chain reaction (PCR) and metagenomic approaches to assess the potential for cyanotoxin and other bioactive metabolite production in Harvey Lake (oligotrophic) and Washademoak Lake (mesotrophic) in New Brunswick, Canada, during summer and early fall months. The PCR survey detected the potential for microcystin (hepatotoxin) and anatoxin-a (neurotoxin) production in both lakes, despite a cyanobacterial bloom only being visible in Washademoak. Genus-specific PCR associated microcystin production potential with the presence of Microcystis in both lakes. The metagenomic strategy provided insight into temporal variations in the microbial communities of both lakes. It also permitted the recovery of a near-complete Microcystis aeruginosa genome with the genetic complement to produce microcystin and other bioactive metabolites such as piricyclamide, micropeptin/cyanopeptolin, and aeruginosin. Our approaches demonstrate the potential for production of a diverse complement of bioactive compounds and establish important baseline data for future studies of understudied lakes, which are frequently affected by cyanobacterial blooms.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".