Subsurface influences on watershed nutrient concentrations and loading in a clay dominated agricultural system
Bibliographic record
Abstract
Water quality and quantity issues persist in the Great Lakes basin (GLB) due to the manipulation of water cycles from anthropogenic and climate stressors. Drinking water contamination, harmful algal blooms and flash flooding are examples of hazards that continue to endanger the wellbeing and stability of communities and ecosystems within the GLB. Continued study of watershed function, particularly in agricultural regions of the GLB, is essential to identify and understand deficiencies in current management and monitoring efforts that contribute to the decline of healthy agroecosystems. The aim of this research is to improve assessment and prediction of hydrologic hazards in agricultural watersheds of the GLB through exploring various field and modelling methods. Specific objectives are to: 1) identify spatial and temporal hydrologic processes that influence groundwater-surface water interactions and nutrient transport using field data and statistical analysis; 2) identify seasonal risk factors of nutrient loss by applying data-driven methods such as principal component analysis and cluster analysis to hydroclimate and nutrient data; and 3) develop an operational framework to apply classification machine learning models to predict local flood warning messages. Research was conducted in two headwater watersheds of the Lake Huron basin within the GLB, the Upper Parkhill watershed and the Harriston watershed, where flooding and water quality issues are a concern. Results from this work indicate that subsurface processes significantly influence nutrient transport and may be an important parameter for high flow forecasting, highlighting the importance of including subsurface components in water quality and quantity studies. Data-driven methods including classification machine learning, unsupervised clustering algorithms and principal component analysis were successfully applied to rural watershed data to uncover important nutrient transport processes and predict flood warning classes with high accuracy. Outcomes from this research may inform future monitoring programs, aid in the development of operations for hazard response, and provide evidence to policy and decision makers for improved water quality and quantity management regulations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".