Toxic Algae Alexandrium catenella Monitoring in Estuary and Gulf of Saint-Lawrence Web Application
Bibliographic record
Abstract
The application shows the first output of the prediction model of the risk of bloom of the toxic alga Alexandrium catenella. The forecasts are updated every six hours and allow to visualize the bloom risk for the next 48 hours. A research program aimed at developing empirical models to predict toxic algal blooms was initiated at the Maurice Lamontagne Institute (Fisheries and Oceans Canada), under the supervision of Michel Starr (PhD), Joël Chassé (PhD), Aude Boivin-Rioux (MSc) and Denis Lefaivre (PhD). The first output of the program is the prediction model of the risk of bloom of the toxic alga Alexandrium catenella. The predictions are updated every six hours and allow to visualize the bloom risk for the next 48 hours. The results of the model can be visualized using the tools developed by the St. Lawrence Global Observatory (SLGO). Thus, forecasts from this first Canadian operational Alexandrium model are made available to federal and provincial government regulatory agencies (e.g. Fisheries and Oceans Canada, Canadian Food Inspection Agency, Ministry of Agriculture, Fisheries and Food), as well as to the aquaculture industry, municipalities, and local populations that depend primarily on marine resources for their livelihood. These organizations directly benefit from the results of this project by obtaining comprehensive information and short-term predictions needed to develop adaptation strategies that minimize the socio-economic impacts of A. catenella. This approach could eventually be extended to other harmful algae species (e.g. Dinophysis) and to other Canadian coastal areas severely impacted (e.g. Strait of Georgia) or potentially impacted (Hudson Bay, Canadian Arctic) by toxic algae.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".