Measurement and simulation of subsurface tracer migration to tile drains in low permeability, macroporous soil
Bibliographic record
Abstract
Abstract Multiyear monitoring and simulation of a conservative tracer was used in this study to investigate preferential flow and macropore‐matrix interactions in low permeability, macroporous soil. 2,6‐Difluorobenzoic acid (DFBA) tracer was applied to a 20 × 20 m drip irrigated test plot situated over two tile drains. Tracer movement over the 2009 and 2010 field seasons was monitored using tile drain effluent, suction lysimeters, monitoring wells, and soil cores. Despite similar volumes of water application to the plot in each season, 10 times more water and 14 times more DFBA were captured by the drains in 2010 due to wetter regional hydrologic conditions. The importance of preferential flow along macropores was shown by rapid DFBA breakthrough to the tile (<47 h), and DFBA detections in sand units below the tile drains. Preferential flow resulted in less than 8% of the DFBA mass being captured by the tiles over both years. With much of the DFBA mass (75%) retained in the upper 0.25 m of the soil at the end of 2009, numerical simulations were used to quantify the migration of this in situ tracer during the subsequent 2010 field season. Dual permeability and dual porosity models produced similar matches to measured tile drain flows and concentrations, but solute leaching was captured more effectively by the dual permeability formulation. The simulations highlighted limitations in current descriptions for small‐scale mass transfer between matrix and macropore domains, which do not consider time‐dependent transfer coefficients or nonuniform distributions of solute mass within soil matrix blocks.
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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 | 0.000 |
| 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 | 0.001 | 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".