Harmful algae and climate change on the Canadian East Coast: Exploring occurrence predictions of Dinophysis acuminata, D. norvegica, and Pseudo-nitzschia seriata
Bibliographic record
Abstract
Harmful algal blooms (HABs) are a threat to human health, local economies, and coastal ecosystems. Generalized additive mixed models (GAMMs) were fitted using a 24-y database in order to predict future occurrences of three distinct species of HABs on the Canadian East Coast, the dinoflagellates Dinophysis acuminata and D. norvegica, and the diatom Pseudo-nitzschia seriata. GAMMs produced for each species were combined with two downscaled climate simulations (MPI-ESM-LR and CanESM2) under the representative concentration pathway (RCP) 8.5 over the 21st century. D. acuminata, D. norvegica, and P. seriata GAMMs were fitted using sea surface salinity and sea surface temperature, with wind speed averaged over seven days added to the P. seriata model. GAMMs succeeded at various degrees at reproducing past HAB events, with D. acuminata and D. norvegica being accurately modelled, and P. seriata producing less precise model results. Both climate simulations lead to similar conclusions in regards to the spatio-temporal shift in occurrences of the three studied species. D. acuminata and D. norvegica blooms (≥ 1000 cells L − 1) are predicted to increase in the future, whereas P. seriata bloom events (≥ 5000 cells L − 1) will tend to stabilise/decrease overall on the Canadian East Coast. Dinophysis blooms are most likely to increase in the St. Lawrence Estuary. Pseudo-nitzschia blooms will move to the northeastern part of the Gulf of St. Lawrence and will increase in the Bay of Fundy/Gulf of Maine regions. On average, earlier blooms and larger seasonal windows of opportunity are predicted across all species investigated. We conclude that changes in D. acuminata, D. norvegica, and P. seriata bloom dynamics and their spatial distributions could threaten aquaculture industries and ecosystem health on Canada's East Coast in localities and during seasons which were not previously impacted by these species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".