Paralytic shellfish poisoning risk assessment in the west coast of Canada
Bibliographic record
Abstract
Paralytic Shellfish Poisonings (PSPs) are a major public health concern requiring robust environmental monitoring. We developed and validated a machine learning framework to assess PSP risk in blue mussels (Mytilus edulis) along Canada's west coast. Our study comprised three experiments that tested the ability of 11 models to forecast risk using historical toxin data (2000-2020). Results showed that lower detection thresholds and the use of multivariate toxin profiles significantly improved predictive accuracy. Tree-based algorithms, in particular, excelled with this detailed data. A stacked ensemble model consistently matched the best individual model's performance, achieving an AUC (area under receiver operating characteristic curve) over 0.912 across all experiments and offering a robust solution for operational forecasting. Model interpretation revealed that recent toxin history and specific compounds like Neosaxitoxin (NEOSTX) and N-sulfocarbamoyl gonyautoxin-3 (C-2) were the most important predictors, aligning with regional ecological dynamics. This framework provides a powerful, data-driven tool for enhancing early warning capabilities and supporting proactive risk management.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".