Reactive Transport of Manure‐Derived Nitrogen in the Vadose Zone: Consideration of Macropore Connectivity to Subsurface Receptors
Bibliographic record
Abstract
Core Ideas Manure N transformation was modeled with different soil pore connectivities to tile drains. Macropores facilitated NH 4 + oxidation reaction products deeper in the profile. Nitrate losses to tile were best predicted by models using pores connected to tile drains. Macropores can be important conduits for surface‐derived nutrients to reach subsurface receptors. Accordingly, nutrient reactive transport processes in macroporous soils need to be well understood. In this study, steady‐state two‐dimensional reactive transport simulations with MIN3P‐THCm (version 1.0.519.0) were used to elucidate how soil macropore connectivity to tile drains can influence N transformations following liquid swine manure (LSM) applications to soil. Four different soil scenarios were considered: homogeneous sand, homogeneous clay loam, and clay loam with discrete macropores connected to or disconnected from the bottom boundary used to represent tile drain outflow. In relation to the homogeneous soils, macropores, overall, facilitated chemical diffusion into the adjacent soil matrix along their length and broadly augmented O 2 ingress into the soil profile. These processes combined to critically control the spatial distribution of NH 4 + oxidation reaction products. When used in transient simulation mode with field data observed at experimental tile‐drained plots that received LSM application, the model showed that simulated nitrate mass losses to tile are considerably higher and most realistic under the connected macropore scenario compared with the homogeneous or disconnected macropore scenarios.
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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 | 0.001 | 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".