Blooming algae: a Canadian perspective on the rise of toxic cyanobacteria
Bibliographic record
Abstract
Algal bloom reports are on the rise across Canada. While eutrophication is the main driver, other stressors of aquatic ecosystems, specifically climate change and food web alterations from the spread of invasive species and overfishing, are compounding factors acting in concert or independently. Current models can predict the average algal and cyanobacterial biomass concentrations across temperate lakes as a function of nutrients, but models to specifically predict harmful algal composition and toxicity are lacking. At the within-lake scale, where management occurs, strong year to year variations in cyanobacterial blooms remain challenging to explain, let alone predict. The most common cyanotoxins, the hepatotoxic microcystins, are chemically diverse with some variants more toxic than others and with greater propensity for persistence and bioaccumulation. These differences have been largely overlooked, as current guidelines have been based on microcystin-LR, considered the most common variant. Microcystin-LA is also encountered in Canadian waters and appears to exhibit greater persistence and bioaccumulation. With cyanobacterial blooms most likely to increase across the country, including the north, guidelines and policies for cyanotoxins in drinking and recreational waters as well as fish will need to be developed for the protection of ecosystem and human health. Ultimately, control of eutrophication is the most important option for managing toxic cyanobacterial blooms; nitrogen and phosphorus need to be considered as environmental contaminants, as both play a role in controlling the dominance of toxigenic cyanobacteria.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".