Bibliographic record
Abstract
Aquatic taste and odor (T/O) is rarely produced by toxic contaminants or pathogens; nevertheless, it has major negative impacts on the public and the drinking-water industry. Consumers use T/O as a primary measure of drinking water safety, yet this criterion is poorly understood, and its origins and triggers often go untraced. Much surface-water T/O is produced by the increased production of volatile organic compounds (VOCs) by algae. These chemicals can be symptomatic of short-term problems with source, treatment, or distribution systems. At a broader level, they can signify fundamental changes in aquatic ecosystems induced by human activity. T/O varies in chemistry, intensity, and production patterns among different algal taxa, and is often linked with excessive algal growth and/or the invasion of noxious species. Some VOCs may signal the presence of potentially toxic algae and/or other associated water quality issues. Traditionally, T/O has been linked with the widespread eutrophication of many surface waters; however, there has been a recent growth in the number of T/O events reported in oligo-mesotrophic systems, for example, the Glenmore Reservoir (Calgary AB) and the Laurentian Great Lakes. From a management and public perspective, therefore, it is vitally important to monitor T/O, and to continue to work toward a better understanding of the proximal and the ultimate causes-which VOCs and algae species are involved. In the short term, odor events could be anticipated and water treatment optimized. In the long term, this approach would contribute toward more a robust management of this resource through remedial or preventative measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".