ALGAL OFF‐FLAVOR COMPOUNDS IN DRINKING WATER: CHEMICAL COMMUNICATION OR CHEMICAL WASTE?
Bibliographic record
Abstract
Watson, S. B. National Water Research Institute, CCIW, P.O. Box 5050, Burlington, ON L7R 4A6 Canada Algae produce a diversity of potent metabolites that can modify surface water chemistry, with major socio‐economic implications. Although non‐toxic to humans, many algal volatile organic compounds (VOCs) cause unpleasant taste and odour (T/O), which undermines consumer confidence and increases their use of alternative and often unregulated drinking water sources. Conventional treatment may fail to remove or even intensify odour, depending on the algal species, the VOCs, and the background levels of organic material present in the source water. Furthermore, some VOCs may signal the presence of potentially toxic algal taxa. On the other hand, many odour‐causing compounds signal changes in growth or metabolism, in community composition, or in ecosystem function and health. Furthermore there is growing evidence that some of these VOCs act as chemical messengers or deterrents (semiochemicals). While many odour compounds are produced by a diversity of algal (and non‐algal) species, there are some general patterns in VOC chemistry and production dynamics among major algal divisions related to cell composition and the metabolic pathways involved. Depending on the VOCs and taxa, production may be intra‐ and/or extracellular, and vary over population cycles with environmental conditions. T/O events therefore can provide opportunities to increase our understanding of chemical interactions among organisms, and how these may lead to, or reflect changes at the level of the individual, the population, and the community.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".