Harmful Algae and Oceanographic Conditions in the Strait of Georgia, Canada Based on Citizen Science Monitoring
Bibliographic record
Abstract
In British Columbia (BC), harmful algal blooms (HABs) regularly cause severe economic losses through finfish mortalities and shellfish harvest closures due to toxin accumulation, gill damage, or hypoxia. As there is no routine governmental monitoring of HAB phenomena in BC, HAB variability, and its potential links to environmental drivers are not well understood. Here we present results from a well-managed citizen science program which collected an unprecedented 4 year, high-resolution (∼bi-monthly, ∼80 stations) dataset of harmful algae (HA) concentrations and corresponding physical and chemical properties of seawater throughout the Strait of Georgia (SoG), BC. Analysis of this dataset revealed statistically significant interannual and seasonal relationships between environmental drivers and the most common HA taxa: Rhizosolenia setigera, Dictyocha spp., Alexandrium spp., Heterosigma akashiwo, Chaetoceros convolutus, and C. concavicornis. HABs exhibited significant interannual variations; specifically, no HABs were found during the summer of 2015, blooms of Dictyocha occurred in 2016 and 2017, and dense blooms of Heterosigma and Noctiluca occurred in 2018. In addition, HA prevalence corresponded with negative effects observed in local aquaculture facilities where higher toxins concentrations (causing Paralytic and Diarrhetic Shellfish Poisonings) in shellfish flesh were detected during years with greater abundance of Alexandrium and Dinophysis. Furthermore, salmon mass mortality at fish farms corresponded to years with high concentrations of Heterosigma and Dictyocha. As such, these results highlight the need for long-term data to evaluate the potential role of HA as a stressor on the SoG ecosystem.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".