Rapidly increasing cyanobacteria blooms in the subarctic Great Slave Lake: observations from Indigenous, local, and scientific knowledge
Bibliographic record
Abstract
Cyanobacteria blooms, typically associated with warm, eutrophic lakes, are increasingly observed in colder, oligotrophic lakes. Cyanobacteria blooms can have ecological impacts and pose health risks when composed of toxin-producing taxa. By combining Indigenous, local, and scientific knowledge sources, we document a profound shift in Great Slave Lake-a huge, remote, oligotrophic lake in Northwest Territories, Canada. Suspected blooms were first observed in 1989, localized near point-source sewage effluent. Since 2009, sporadic blooms have appeared in new areas of the North Arm, away from known point source nutrients, and become increasingly frequent. By 2020, bloom density increased, with the densest and most widespread blooms observed in 2024. These blooms have generally been nearshore and transient, most frequently located in sheltered waters, which are warmer and shallower relative to the rest of the lake. Dolichospermum is the dominant genus, with no microcystin toxins detected. We hypothesize these unprecedented blooms may be climate-driven, enabled by a combination of warmer water, reduced wind and ice cover, and potentially fueled by nutrient inputs from record water levels, intensified wildfires, permafrost thaw, and cultural eutrophication. By synthesizing across knowledge systems, we establish a foundation for collaborative research and monitoring in rapidly changing northern water bodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".