Direct and interactive effects of climate, meteorology, river hydrology, and lake characteristics on water quality in productive lakes of the Canadian Prairies
Bibliographic record
Abstract
Aquatic ecosystems are subject to multiple interacting stressors that obscure regulatory mechanisms and reduce the effectiveness of management strategies. Here we estimate the unique and interactive effects of continental climate systems, regional meteorology, river hydrology, and internal lake characteristics on patterns of landscape-scale water quality in six productive lakes within a 52 000 km2 catchment. We quantify variation in mean summer and monthly algal abundance, surface bloom intensity, water clarity, and density of potentially toxic cyanobacteria during 16 years on the Canadian Prairies. Internal lake characteristics best predicted overall water quality change, while climate systems, regional weather, and river hydrology characterized indirect pathways that influenced physicochemical environments. Scenario analysis of future environmental change predicted that atmospheric warming (3–5 °C) will have the strongest effect on water quality in these productive lakes, but unexpectedly predicted that even severe industrial water extraction (1% of inflow) will have negligible effects on transparency or algal abundance. Instead, nutrient management represents the only practical means to sustain water quality, although atmospheric and lake warming may override re-oligotrophication of eutrophied sites in future decades.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".