Predicting the risk of proliferation of the benthic cyanobacterium<i>Lyngbya wollei</i>in the St. Lawrence River
Bibliographic record
Abstract
Lyngbya wollei is a toxin-producing cyanobacterium, forming mats of filaments that proliferate in rivers, lakes, and springs in North America. We determined its distribution over a 250 km stretch of the St. Lawrence River (SLR; Quebec, Canada) to elaborate predictive models of its presence and biomass based on chemical and physical characteristics. A 2008 survey revealed L. wollei was generally found downstream of the inflow of small tributaries draining farmlands. As enriched waters flowed slowly through dense submerged vegetation, dissolved inorganic nitrogen (DIN) concentration dropped but dissolved organic carbon (DOC) and total dissolved phosphorus (TDP) remained high, leading to a low DIN:TDP ratio. Models identified DOC (positive effect), TP (negative effect), and DIN:TDP (negative effect) as the most important variables explaining L. wollei distribution. The risk of L. wollei occurrence in the SLR was correctly forecasted in 72%–92% of all cases with an independent data set. Proliferation of L. wollei, a potentially heterotrophic, diazotrophic cyanobacterium, is favoured by organic enrichment and imbalance of DIN:TDP ratio. Its dominance coincided with low macrophyte biomass, yielding a simplified, less productive ecosystem.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".