Off flavours in large waterbodies: physics, chemistry and biology in synchrony
Bibliographic record
Abstract
The Laurentian Great Lakes of North America are a drinking water source for millions of Canadian and US consumers. These waterbodies have undergone extensive change over the past century as a result of widespread degradation and remediation. Many of the Lakes are prone to taste and odour (T&O), and although these outbreaks have been poorly monitored, evidence suggests that they are increasing in frequency. Tracing and controlling T&O in such large systems presents a challenging task, due to their physical size and complexity. This paper presents an overview of recent investigative and management approaches to T&O in Lake Ontario and its outflow, the St. Lawrence River. We have identified three distinct patterns of T&O in these source-waters, caused by geosmin and 2-methylisoborneol and differing in their planktonic and benthic sources, and temporal and spatial dynamics. Each pattern has required a different approach by scientists and management, in partnership with the water industry. We have shown these T&O outbreaks are caused and moderated by physical, chemical and biological mechanisms over a spectrum of spatial and temporal scales. Canadian municipalities affected by these outbreaks have been key to the investigation of the links between T&O and ecosystem processes with the aim to develop more proactive water treatment and long-term management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".