Causal discovery of hydroclimatic drivers influencing water quality in a large lake
Bibliographic record
Abstract
Abstract Harmful algal blooms (HABs) are a threat to ecosystem services, with adverse economic and public health impacts. Large-scale climate processes will influence local environmental conditions, potentially favoring HAB formation through complex, nonlinear interactions. This study employs explainable (i.e., SHAP) machine learning to give insight on the predictions and causal analysis (i.e., PCMCI) to identify the relationships between climate indices, physical drivers, and the resultant Chlorophyll-a (CHL) concentrations in western Lake Erie. Our causal analysis revealed that runoff and water temperature directly affect CHL but also act to mediate the impacts of the Arctic Oscillation on influencing CHL. Moreover, our explainable analysis further confirmed this by identifying runoff as the main driving factor, followed by water temperature. The study highlights that water quality in the basin is subject to confounding effects resulting from interactions between global atmospheric circulation patterns and local hydro-meteorological factors, expanding HAB forecasting beyond synoptic scale meteorology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".