Exploring the Trends in Sediment and Phosphorous Concentration and Loads: Case Study over Part of the Great Lake Basin (Ontario, Canada)
Bibliographic record
Abstract
Abstract In this study, trend analysis of total suspended solids (TSS) and total phosphorus (TP) concentrations and loads are performed, combining bootstrapping with the Weighted Regressions on Time, Discharge, and Season i.e., WRTDS_BT technique. The technique is used at ten selected monitoring stations of Northern Lake Erie, Eastern Lake Huron, and Lake Ontario & Niagara Peninsula in Ontario, Canada. Trend analysis over major tributaries reveals that trends in TSS concentrations and loads were highly variable, while there was a significant decline in TP concentrations and loads. However, in most tributaries, TSS and TP concentration levels are significantly higher than the provincial and national guidelines and objectives. To be precise, TSS concentration levels are significantly higher than Canadian Water Quality Guidelines (CWQG) for the TSS concentration in tributaries of 30 mg/L (following Toronto Region Conservation Authority (TRCA), Ontario) and TP concentration levels are significantly higher than the Ontario’s provincial water quality objectives (PWQO) guidance for the TP concentration in tributaries of 0.03 mg/L. Moreover, our findings suggest that changes in land management practices in agricultural areas, such as tillage, tile drainage and fertilizer/manure application may play an important role for the analysed trend.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".