Toxic Cyanobacterial Bloom Triggers in Missisquoi Bay, Lake Champlain, as Determined by Next-Generation Sequencing and Quantitative PCR
Bibliographic record
Abstract
Missisquoi Bay (MB) is a temperate eutrophic freshwater lake that frequently experiences toxic Microcystis-dominated cyanobacterial blooms. Non-point sources are responsible for the high concentrations of phosphorus and nitrogen in the bay. This study combined data from environmental parameters, E. coli counts, high-throughput sequencing of 16S rRNA gene amplicons, quantitative PCR (16S rRNA and mcyD genes) and toxin analyses to identify the main bloom-promoting factors. In 2009, nutrient concentrations correlated with E. coli counts, abundance of total cyanobacterial cells, Microcystis 16S rRNA and mcyD genes and intracellular microcystin. Total and dissolved phosphorus also correlated significantly with rainfall. The major cyanobacterial taxa were members of the orders Chroococcales and Nostocales. The genus Microcystis was the main mcyD-carrier and main microcystin producer. Our results suggested that increasing nutrient concentrations and total nitrogen:total phosphorus (TN:TP) ratios approaching 11:1, coupled with an increase in temperature, promoted Microcystis-dominated toxic blooms. Although the importance of nutrient ratios and absolute concentrations on cyanobacterial and Microcystis dynamics have been documented in other laboratories, an optimum TN:TP ratio for Microcystis dominance has not been previously observed in situ. This observation provides further support that nutrient ratios are an important determinant of species composition in natural phytoplankton assemblages.
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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 | 0.001 |
| 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".