Contribution of preferential flow to tile drainage varies spatially and temporally
Bibliographic record
Abstract
Abstract Tile drainage of agricultural fields is a conduit for nutrient losses. Preferential flow in the soil can more directly connect surface runoff with tile drainage compared with matrix flow, which may also increase P losses. In this study, water temperature was monitored in surface runoff and tile drainage and electrical conductivity (EC) was measured in tile drainage at two sites in southern Ontario with different soil types (i.e., clay and loam). These data were used to estimate the percentage of preferential flow in tile drainage based on end member mixing. Estimates using temperature were compared with estimates using EC, and both were evaluated across seasons and hydrographs and compared with P concentration and load data. There was strong correlation ( r = .83) between estimates of preferential flow using the two methods, but due to variability in surface temperatures, EC provided a less flashy estimate for preferential flow (Durbin–Watson statistics of 0.34 for temperature and 0.09 for EC). Preferential flow accounted for a higher percentage of tile drainage flow in clay soil than loam, but percentages were not significantly different between seasons or timing within events. Phosphorus concentrations and loads were weakly correlated with preferential flow, suggesting that P transport was influenced by other factors as well. Although further work is necessary to calibrate these methods for estimating preferential flow from continuously monitored temperature and EC, this technique can be applied to already collected data to model and test posited explanations of observed phenomena in P, other nutrients, and water transport from tile‐drained agricultural land.
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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 | 0.000 | 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 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".