Taste and odour and cyanobacterial toxins: impairment, prediction, and management in the Great Lakes
Bibliographic record
Abstract
This paper reviews the issues associated with algal–cyanobacterial taste–odour (T&O) compounds and toxins in the Great Lakes. As with other remediated water bodies, the Great Lakes have undergone significant shifts in nutrient and food-web regimes and are exhibiting erratic blooms and noxious algal metabolite (NAM) outbreaks, despite reduced offshore nutrient levels. We appraise the chemistry, biota, and distribution of NAM impairments and conclude that management strategies based on lakewide monitoring and remedial action plans are often unsuccessful because they attempt NAM control through an unsustainable reliance on water treatment and broad-scale nutrient–biomass models. This approach is undermined by several factors: (i) only some species produce NAMs; (ii) different taxa show disparate patterns across nutrient and mixing regimes; (iii) nuisance species may be planktonic or benthic and located outside remedial boundaries; and (iv) species differ significantly in NAM biochemistry and release. Thus, there are no robust relationships between total plankton biomass, toxins, and T&O compounds in these and other source waters. Given the potential ecological and socioeconomic threats posed by NAM outbreaks, there is a critical need to develop a multistep management framework based on more stringent restoration targets, combining broad-scale screening and nutrient management with system and taxa-specific approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".