Using RZWQM2‐P to capture tile drainage phosphorus dynamics in Ohio
Bibliographic record
Abstract
Abstract Phosphorus (P) loading from tile‐drained agricultural lands is linked to water quality and aquatic ecosystem degradation. The RZWQM2‐P model was developed to simulate the fate and transport of P in soil‐water‐plant systems, especially in tile‐drained croplands. Comprehensive evaluation and application of RZWQM2‐P, however, remains limited. This study evaluates RZWQM2‐P in simulating P dynamics using extensive data and assesses the potential of management practices for mitigating P losses. Subsurface drainage and surface runoff flows were monitored at a tile‐drained site from 2017 to 2020 in Ohio, and the water flow and P loss data were summarized on a daily basis. RZWQM2‐P was calibrated and validated using those observed data and was subsequently used to assess the effectiveness of controlled drainage (CD) and winter cover crops (CC) in reducing P losses. The model satisfactorily simulated dissolved reactive P (DRP) loss from tile drainage on daily and monthly bases (Nash–Sutcliffe efficiency [NSE] = 0.50, R2 = 0.52, index of agreement [IoA] = 0.84 for daily; NSE = 0.73, R2 = 0.78, IoA = 0.94 for monthly) and total P (TP) loss on a monthly basis (NSE = 0.64, R2 = 0.65, IoA = 0.88), but the daily TP simulation was less accurate (NSE = 0.30, R2 = 0.30, IoA = 0.59). Simulations showed that winter rye CC reduced DRP by 16% and TP by 4% compared to the base scenario, whereas CD increased DRP (60%–129%) and TP (5%–17%) losses at three tested outlet elevations compared to free drainage. RZWQM2‐P can capture P dynamics in tile‐drained cropland and is a promising tool for effective P management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".