Toxic Algae Alexandrium catenella Monitoring in Estuary and Gulf of Saint-Lawrence Web Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it