Groundwater table prediction and seasonal variation influenced by short rotation willow plantation on marginal riparian lands of the Prairie potholes in Canada
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Bibliographic record
Abstract
Shallow groundwater consumption via phreatophytic transpiration and resulting vegetation-linked groundwater table (GWT) fluctuation is a typical soil hydrological process in wetland riparian areas. However, upland and riparian land use alterations may further influence the shallow GWT fluctuation, temporally and spatially. In this multi-year field study, we investigated whether introducing short rotation willow (SRW) positively or negatively affects the shallow GWT, soil water availability, and soil health on marginal riparian lands of the Prairie Pothole Region (PPR). We compared the impact of SRW on these parameters to two common land uses: annual crop (AC) and pasture (PA). Depth to GWT was monitored via data loggers from 28 wells in two semi-arid PPR sites. The GWT depth varied by land use practices only in site B ( p < 0.001; PA > SRW = AC) but not significantly in site A ( p = 0.325), and the patterns were inconsistent between sites. In GWT depth prediction, the performance of Artificial Neural Network (ANN) was better than Autoregressive Integrated Moving Average (ARIMA) models but was inconsistent alike with field observations. The GWT depth responded to seasonal precipitation and potential evapotranspiration (ET) patterns. The monthly GWT fluctuations peaked between June and August due to increased precipitation, while they were lower during May and September with reduced precipitation; however, these variations were not significant ( p > 0.05). Higher precipitation and lower potential ET throughout the wet year (i.e., in 2014) significantly ( p < 0.05) raised GWT (i.e., decreased depth to GWT) under all land uses, and vice versa. Our study indicated that planting SRW in marginal riparian land of the PPR would not negatively impact shallow GWT or soil water availability. Moreover, the SRW plantation could also help manage soil salinity without severely depleting the soil's nutrient pools or diminishing soil quality and health indicator parameters measured during the first rotation.
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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 | 0.000 | 0.000 |
| 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 it