Simulating phosphorus loss to subsurface tile drainage flow: a review
Bibliographic record
Abstract
Agricultural land is a major source of phosphorus (P) loss, and artificial drainage is one of the pathways for phosphorus transport. In this paper, we reviewed the methods and equations related to phosphorus loss through subsurface tile drain in water quality models. This review is presented through three topics: subsurface hydrology, fate and transport of phosphorus in soil, and phosphorus transport into tile drains. Major simulation methods and some recent updates are reviewed, and calculations in specific models are presented. Nine existing water quality models (ADAPT, ANIMO, APEX, EPIC, HYDRUS, ICECREAM, MACRO, PLEASE, SWAP) can be used to simulate P transport to tile drainage, where three of them (HYDRUS, MACRO, SWAP) do not have a specific phosphorus module but P can be simulated using a general chemical module. Models that are not suitable for simulating fate and transport of P to tile drains under their current status, for example, AnnAGNPS, DRAINMOD, GLEAMS, RZWQM2, SurPhos, SWAT, are also reviewed due to their strength in one of the aspects: subsurface drainage or P dynamics. Based on the methods used in those models, ICECREAM could be the most current comprehensive model for P loss through tile drains from agricultural fields.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".