Tracking Trouble: Spatial and Temporal Distribution of Potentially Harmful Algae Species in Clayoquot Sound, BC
Bibliographic record
Abstract
Algal species, such as Alexandrium spp., Pseudo-nitzschia spp. and Dinophysis spp., are known to cause harmful blooms (HABs), where toxins accumulate in bivalves and can lead to paralytic, amnesic, and diarrhetic shellfish poisoning, respectively, in humans. These blooms disrupt aquaculture industries and can cause significant economic loses. Additionally, some harmful algae may damage marine animals, including causing fish kills, and disrupt trophic interactions, threatening overall ecosystem stability. Increasing frequency and severity of blooms have been correlated with higher nutrient loads and may also be influenced by changing water properties, such as temperature. Since 2001, researchers at the University of Washington Tacoma have collected late summer/early fall water property data in Clayoquot Sound, with phytoplankton sampling added beginning in 2006. Phytoplankton samples from vertical net tows and discrete water samples collected at 1m and 10m were analyzed taxonomically to quantify species’ presence and concentrations. This study investigates the spatial and temporal distribution of select potentially harmful algal species in Clayoquot Sound to assess their prevalence and potential risks to human health, aquaculture, and local economies. We compiled and mapped distribution patterns of the three target species and evaluated changes over the past 18 years, comparing findings with historical data from Department of Fisheries and Oceans Canada. We hypothesize that HABs will become increasingly prevalent as water temperatures rise, as warmer conditions can enhance algal growth rates and extend bloom seasons. The results will support future monitoring and management strategies to mitigate HABA impacts.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".