Off flavours in large waterbodies: physics, chemistry and biology in synchrony
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 it