Dolichospermum blooms in Lake Superior: DNA-based approach provides insight to the past, present and future of blooms
Bibliographic record
Abstract
Cyanobacterial blooms are increasing in frequency, duration, and severity globally in freshwater ecosystems. The Laurentian Great Lakes are prone to toxin-producing cyanobacterial blooms and have experienced annually recurring blooms. Because of its oligotrophic nature, Lake Superior has been relatively free of bloom occurrences. However, in recent years, Dolichospermum blooms have occurred with increasing frequency, especially in the western arm. During a Dolichospermum bloom in 2018, opportunistic samples were collected from the offshore bloom and investigated with shotgun metagenomics. We identified a near-complete Dolichospermum genome that is highly similar to genomes from cultures recovered in Lakes Erie and Ontario. The genomes from the Laurentian Great Lakes are typified by their putative ability to produce a suite of secondary metabolites like anabaenopeptin, but not toxins like microcystin. Additionally, we recovered a Dolichospermum lemmermannii 16S rRNA gene from the bloom and using datasets collected from the epilimnion and sediments in Lake Superior show this organism is ubiquitous and that several strains may exist. While there is much to learn about Lake Superior cyanobacterial bloom development and triggers, understanding this organism is endemic to the region, what its genome is capable of and that specific strains may have provenance within the lake provides a distinct ecological basis for understanding and working towards a predictive framework for future 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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".