The Evaluation of Statistical Models in Water Quality Constituents Load Estimation in Southern Ontario, Canada
Bibliographic record
Abstract
The accurate representation of the water quality constituents load transported by rivers and streams is crucial to understand the quality of the lakes, the behavior of the rivers, to assess the efficacy of water quality monitoring projects, and for the development of watershed models. Numerous statistical models have been developed to predict the water-quality constituent loads from the available data of sampled concentration and continuous discharge at a particular sampled location and to further analyze trends and changes in water quality. However, the performance of statistical models to estimate water quality constituent loads depends on many aspects including the type of water quality constituent, discharge-concentration relationship, sampling strategy and frequency, and the watershed area. This study evaluates the performance of a wide range of statistical models for the estimation of total suspended solids (TSS) and total phosphorus (TP) loads under various sampling scenarios and monitoring stations in Southern Ontario, Canada. Trends in TSS and TP concentrations and loads were further analyzed in major tributaries. The Weighted Regression on Time, Discharge, and Season Kalman Filter (WRTDS_K) model was found to be the most suitable model for predicting TSS loads at most sampling stations and under most sampling scenarios, while, the Weighted Regression on Time, Discharge, and Season (WRTDS) model was found to be the most suitable model for predicting TP loads. No statistical models showed the potential to predict accurate load estimates at monitoring stations with small drainage areas. Trend analysis over major tributaries revealed that trends in TSS concentrations and loads were found to be highly variable among tributaries, while, there was a significant decline in TP concentrations and loads over major tributaries. However, TSS and TP concentration levels in most tributaries were found to be significantly higher than the PWQMN objectives.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".