Microseira wollei (M. wollei) blooms in freshwater ecosystems in Lake St. Clair (Michigan, USA)–impacts and possible management approaches
Bibliographic record
Abstract
The proliferation and shoreline accumulation of the filamentous biphasic cyanobacterium, Microseira wollei (M. wollei) (previously classified as Lyngbya wollei), have become an increasing problem in the Great Lakes, both for aesthetic reasons and its potential to harbor harmful bacteria and pathogens (Vijayavel et al. 2013). Occurrences have been reported and studies have also been conducted in the southeastern US where M. wollei has become a nuisance in recent years and is known to produce toxins (Hudon et al. 2014). Reports of M. wollei proliferations in the eastern US have been identified in the Manitoba lakes (Macbeth 2004), in Lake Erie from Maumee Bay (Bridgeman and Penamon 2010), in Lake St. Clair near Detroit (Vijayavel et al. 2013), and throughout the St Lawrence River (Vis et al. 2008; Lévesque et al. 2012). M. wollei has become a serious nuisance for marinas, public beaches, and lakefront property owners. In addition, M. wollei appears to have the ability to produce a wide range of toxins, but the conditions promoting their production, type, and concentration are poorly known (Hudon et al. 2014). Occurrences of large algal mats matching characteristics of M. wollei have been observed along the northwest shore and nearshore waters of the beach at Lake St. Clair dating back to 2010. To date, a comprehensive study detailing the potential impacts M. wollei has on freshwater ecosystems in the Great Lakes River, particularly Lake St. Clair is lacking. Further, management solutions are not well understood. This technical note (TN) reviews the potential causes of M. wollei blooms and their ecological impacts on aquatic systems and assesses the management options available to eliminate or minimize the impacts of these 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 | 0.001 |
| 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".