The Occurrence of Cyanotoxins in the Nearshore and Coastal Embayments of Lake Ontario
Bibliographic record
Abstract
Lake Ontario, as many other lakes in the world, has suffered from a variety of environmental problems over the past fifty years. Massive blooms followed by die-backs of Cladophora, a filamentous algae, and by diebacks of the alewife, a fish, fouled beaches along the Lake Ontario shoreline in the 1960s. The phosphorus abatement program, agreed to by both Canada and the United States, successfully reduced the levels of phosphorus that stimulated the growth of algae. As a result of the reduction of phosphorus, once commonly found in detergents and sewage plant effluent, algae populations including the once ubiquitous shoreline Cladophora species were reduced by the late 1980s. Similarly with the introduction of the alewife-eating Pacific Salmon [11], lake alewife
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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 | 0.000 | 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 | 0.000 |
| 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".