Empirical study of cyanobacterial toxicity along a trophic gradient of lakes
Bibliographic record
Abstract
A series of 22 lakes in southern Quebec spanning a wide trophic range were sampled to develop models of changes in cyanobacterial abundance and toxicity. All lakes contained toxic cyanobacteria, and epilimnetic toxin content, expressed as microcystin equivalents, was best predicted by total nitrogen concentration and total phosphorus concentration (TP). Although phytoplankton biomass increased linearly with increases in TP among lakes, toxigenic biomass increased as greater than the squared power of TP. The only potentially toxigenic genera whose biomass was correlated with microcystin concentration were Microcystis and Anabaena. Surprisingly, the best model for toxic-species biomass was based on epilimnetic nitrogen. The level of the hepatotoxin microcystin per unit biomass in these organisms did not vary markedly among lakes, supporting the idea that environmental factors control the occurrence, but have only a limited effect on the toxicity, of potentially toxic species.
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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) | 0.000 | 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 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".