Quantifying microcystin concentrations, their composition and drivers across over 400 north-temperate and boreal Canadian lakes
Bibliographic record
Abstract
Microcystins (MCs) are the most commonly measured of the cyanotoxins. Roughly 250 MC congeners have been identified to date with noted differences in their toxicity and persistence. Regional and national scale models for different parts of the world have been instrumental in identifying the drivers of total MC concentrations including toxin-producing cyanobacteria biomass, nutrients (total nitrogen and phosphorus), agricultural development in the watershed, temperature and light. Most of the MC work in Canadian lakes has focused on total concentrations and congener-specific data are lacking. Using the first Canada-wide lake set, with standardized sampling generated through the NSERC LakePulse Network, we quantified the importance of biotic and abiotic predictors of total MCs as well as several congeners from up to 440 lakes. Overall, MCs were detected in 30% of lakes using ELISA, mostly in central Canada within the Prairies and Boreal Plains ecoregions. Total concentrations were generally low, with just 10% of lakes exceeding the WHO drinking water guideline. While considering a broad suite of variables – physiography, water quality, land use, zooplankton abundance, climate, and cyanobacteria enumerations - MCs were most associated with high nutrients, low hypolimnetic oxygen, high chlorophyll-a and the biomass of known MC producers, particularly Microcystis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".