Periphyton: a primary source of widespread and severe taste and odour
Bibliographic record
Abstract
In the last decade, a late summer-fall taste and odour problem has been a prolonged and annual event in the St Lawrence River (SLR). Earlier work identified the earthy/musty compounds geosmin and particularly, 2-methylisoborneol (GM-MIB), and ruled out Lake Ontario as a major source, but did not identify the biological origins. In 2000, we investigated the source(s) and underlying causes. We sampled littoral sites in the SLR near Cornwall, ON, and found that macrophytes (or associated biofilms) may be primary GM sources. Zebra mussel homogenate yielded low GM-MIB levels, but several associated actinomycetes generated high in vitro amounts. Periphyton from rocks showed significant yields, with cell-bound GM-MIB up to one hundred times the levels in overlying water. In 2001, we followed seasonal changes at some of these sites. Periphyton GM-MIB showed intriguing spatial and temporal patterns. Several cyanobacteria in these biofilms were identified as potential odour sources, notably Oscillatoriales. We conclude: i) periphyton is a major odour source in the SLR; ii) other biota such as macrophytes and mussels may also contribute; iii) seasonality in GM-MIB production and ratios indicate changes in cell production and/or taxa in response to environment. These results may account for the recent onset of the problematic odour events, which represent chemical signals of the increased water transparency and littoral surface area following the widespread dreissenid mussel invasion to the Great Lakes. Our data raise key questions about the processes that trigger the tremendous variability in biota and GM-MIB production in the SLR, the subject of our continued research.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".