Profiling marine biotoxins in the Salish Sea
Bibliographic record
Abstract
The frequency and magnitude of marine harmful algal blooms (HABs) appear to be increasing worldwide, influenced by factors such as eutrophication and climate change. Biotoxins produced by harmful algae can have a negative impact on marine life including fish, mammals and seabirds. Variations in the timing, extent, duration, and impact of toxic HABs have been linked to changing environmental conditions, including extreme events such as the 2014-2016 North Pacific marine heatwave. Scientists at Fisheries and Ocean Canada (DFO) have been partnering with the Pacific Salmon Foundation (PSF) Citizen Science program to collect and analyze biotoxin samples, taxonomic and environmental data from the Salish Sea. The goal of this research is to identify the biotoxins present in these waters and the environmental conditions associated with their production by harmful algae. To enable this research, new methodology has been developed to quantify multiple biotoxins in seawater and phytoplankton, including those associated with amnesic, paralytic and diarrhetic shellfish poisoning in humans. We have used this innovative approach to generate spatial and temporal profiles of harmful algal biotoxins in the Salish Sea, an area that encompasses aquaculture facilities, coastal communities, and critical habitat for (endangered) marine mammals and their prey. Our results suggest that biotoxin concentrations are related to water temperature as well as the presence of associated harmful algae. This information may be used to help predict and manage the impacts of toxic algal blooms on the Salish Sea Ecosystem in a changing climate.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".