Influence of Tillage System on Water Quality and Quantity in Prairie Pothole Wetlands
Bibliographic record
Abstract
Since zero tillage (ZT) requires more herbicide and fertilizer use than conventional tillage (CT) and may improve water infiltration into soil, the system may negatively impact prairie pothole wetlands. In this paper, the hydrology and water quality of pothole wetlands in zero tillage and conventional tillage systems were compared by monitoring three wetlands (ZT-1, ZT-2 and CT) from 1995 to 1997, and during a runoff-producing summer storm in 1998. Wetland water levels were recorded during snowmelt runoff and throughout the unfrozen period. Water samples from the wetlands were analyzed for total P, ortho P, NO2-NO3, NH3 and a suite of commonly-used herbicides. In each year of the study, similar snow accumulations generated more runoff per unit area from the ZT basins than the CT basin. Water levels were similar in the three wetlands in the spring of 1995, but by 1997 the water depths were less in the ZT wetlands than in the CT wetland. Despite greater fertilizer use in the ZT basins, we did not observe a consistent effect of tillage system on available N and P in the surface soil. Phosphorus concentrations were generally higher in the ZT than the CT wetlands during snowmelt but there was no consistent effect of tillage on NO2-NO3 or NH3 concentrations in the wetlands. The herbicides found in all three wetlands included those that were applied during the study and some that were not. At least one herbicide was detected in trace amounts in approximately 75% of samples from the wetlands.
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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 | 0.000 | 0.000 |
| 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 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".