Phytoplankton blooms in Lake Winnipeg linked to selective water-gatekeeper connectivity
Bibliographic record
Abstract
Lake Winnipeg was coined "Canada's sickest lake" and "the most threatened lake in the World" due to its recurrent algal blooms caused by nutrient-rich water inputs. While conceptual frameworks link bloom occurrence to hydrologic connectivity, data-based validation is lacking. We analyzed 355 multi-year satellite-derived images to quantify phytoplankton biomass in Lake Winnipeg and the timing of runoff activation and hydrologic connectivity in the Lake Winnipeg Watershed. Our analyses reveal that the majority of watershed runoff-producing areas exhibit a strong coupling between runoff activation and hydrologic connectivity: they are proximal to rivers and become hydrologically connected to them multiple times a year. Conversely, a smaller number of runoff-producing areas are located further upslope and connect to rivers much less frequently. The latter act as water gatekeepers by selectively enabling the downstream transfer of runoff from headwater regions. Major blooms in Lake Winnipeg only occur when 50% of the water gatekeepers enable headwater-downstream connectivity during 31.5% (or more) of the spring-fall period. We conclude that an explicit assessment of the timing of runoff activation and hydrologic connectivity serves as a predictor of bloom occurrence and provides new information about the influence of a small number of locations on Lake Winnipeg.
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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 | 0.001 |
| 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".