Assessment of Water Quality Trends in the Minnesota River using Non‐Parametric and Parametric Methods
Bibliographic record
Abstract
Excessive loading of sediment and nutrients to rivers is a major problem in many parts of the United States. In this study, we tested the non-parametric Seasonal Kendall (SEAKEN) trend model and the parametric USGS Quality of Water trend program (QWTREND) to quantify trends in water quality of the Minnesota River at Fort Snelling from 1976 to 2003. Both methods indicated decreasing trends in flow-adjusted concentrations of total suspended solids (TSS), total phosphorus (TP), and orthophosphorus (OP) and a generally increasing trend in flow-adjusted nitrate plus nitrite-nitrogen (NO(3)-N) concentration. The SEAKEN results were strongly influenced by the length of the record as well as extreme years (dry or wet) earlier in the record. The QWTREND results, though influenced somewhat by the same factors, were more stable. The magnitudes of trends between the two methods were somewhat different and appeared to be associated with conceptual differences between the flow-adjustment processes used and with data processing methods. The decreasing trends in TSS, TP, and OP concentrations are likely related to conservation measures implemented in the basin. However, dilution effects from wet climate or additional tile drainage cannot be ruled out. The increasing trend in NO(3)-N concentrations was likely due to increased drainage in the basin. Since the Minnesota River is the main source of sediments to the Mississippi River, this study also addressed the rapid filling of Lake Pepin on the Mississippi River and found the likely cause to be increased flow due to recent wet climate in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".