Evaluating Hydrologic Response in Tile‐Drained Landscapes: Implications for Phosphorus Transport
Bibliographic record
Abstract
Phosphorus (P) loss in agricultural discharge has typically been associated with surface runoff; however, tile drains have been identified as a key P pathway due to preferential transport. Identifying when and where these pathways are active may establish high‐risk periods and regions that are vulnerable for P loss. A synthesis of high‐frequency, runoff data from eight cropped fields across the Great Lakes region of North America over a 3‐yr period showed that both surface and tile flow occurred year‐round, although tile flow occurred more frequently. The relative timing of surface and tile flow activation was classified into four response types to infer runoff‐generation processes. Response types were found to vary with season and soil texture. In most events across all sites, tile responses preceded surface flow, whereas the occurrence of surface flow prior to tile flow was uncommon. The simultaneous activation of pathways, indicating rapid connectivity through the vadose zone, was seldom observed at the loam sites but occurred at clay sites during spring and summer. Surface flow at the loam sites was often generated as saturation‐excess, a phenomenon rarely observed on the clay sites. Contrary to expectations, significant differences in P loads in tiles were not apparent under the different response types. This may be due to the frequency of the water quality sampling or may indicate that factors other than surface‐tile hydrologic connectivity drive tile P concentrations. This work provides new insight into spatial and temporal differences in runoff mechanisms in tile‐drained landscapes. Core Ideas Activation of surface runoff and tile flow differ with soil texture and season. Timing of flow path activation was used to infer hydrological processes. Connectivity between the surface and tiles exists on clay soil during growing season. Rapid connectivity between the surface and tiles occurs less frequently on loam.
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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 | 0.003 | 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".