Literature review of Microseira wollei distribution, environmental drivers, and risks : Lake St. Clair, Michigan, case study
Bibliographic record
Abstract
Microseira wollei (formerly Lyngbya wollei) has grown to noxious densities within Lake St. Clair, located between Lake Erie and Lake Huron. De-spite the limited data on this cyanobacterium within Lake St. Clair, data exists for M. wollei within the Great Lakes region and in the southeastern United States, where water resource managers have been managing growths for decades. These data provide pertinent insights into the environmental distribution, environmental drivers, risks, and management of M. wollei, which is mainly distributed within eastern states and provinces in North America, from Canada to Florida. Environmental drivers may be site-specific and specific to the M. wollei population; therefore, the environmental drivers identified in this literature review are a starting point to inform further investigations. M. wollei within Lake St. Clair may pose risks to humans. Risks may originate from toxins, disinfection by-products, and, potentially, fecal indicator bacteria. M. wollei has the potential to produce a range of toxins; however, the most prevalent toxins are saxitoxins, a group of neurotoxins. This literature review will help the US Army Corps of Engineers Detroit District; Macomb County, Michigan; and other interested parties understand potential triggers for growth, communicate risks, and help develop an adaptive management framework.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".