The link between precipitation, river runoff, and blooms of the toxic dinoflagellate <i>Alexandrium tamarense</i> in the St. Lawrence
Bibliographic record
Abstract
Blooms of the toxic dinoflagellate Alexandrium tamarense, which is responsible for paralytic shellfish poisoning, are annually recurrent events in the Estuary and Gulf of St. Lawrence, Québec, Canada. The analysis of abundance data for this algal species between 1989 and 1998 at Sept-Îles, a presumed initiation site in the north western Gulf of St. Lawrence, revealed yearly fluctuations in the onset, duration, and magnitude of toxic A. tamarense blooms. Hydrological and meteorological data for the region indicate that rainfall, Moisie River runoff, and wind are highly related to the pattern of bloom development each year. Results from the 10-year data set reveal that in this system: (i) high Moisie River runoff from a prolonged spring freshet or from heavy rainfall events in the summer and fall can initiate A. tamarense blooms; (ii) high Moisie River runoff combined with prolonged periods of weak winds (<4 m·s1) favour the continued development of blooms; and (iii) winds >8 m·s1 disrupt blooms. Salinity, which reflects the general state of the water column in terms of freshwater input and stability, had a strong negative correlation with the probability of observing A. tamarense cells at this station and could thus be used as a predictive tool for the presence of cells in this system.
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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 | 0.000 | 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) | 0.001 | 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".