Assessing water and nitrate‐N losses from subsurface‐drained paddy lands by DRAINMOD‐N II
Bibliographic record
Abstract
Abstract In this study, the effects of various drainage systems on water and nitrate‐N losses were investigated using DRAINMOD‐N II. Required field data were obtained during three growing seasons of canola in a subsurface drainage pilot in Mazandaran Province, northern Iran. The calibrated model was used to assess the effects of different drain depths (D = 0.10–0.90 m with 0.10 m intervals) and spacing (L = 10–90 m with 10 m intervals) on seasonal drainage water and NO3−‐N concentration in drainage effluents. DRAINMOD‐N II performance was assessed using different criteria including absolute deviation (AD), root mean square error (RMSE) and determination coefficient (R2). The simulated and observed drainage discharges (0.97 vs 0.96 mm day−1) and NO3‐N concentrations (9.1 vs 14.1 mg l−1) were in good agreement in the calibration process. The model performance was also acceptable in the validation process (AD = 0.59–0.79 mm day−1; RMSE = 1.01–1.28 mm day−1; R2 = 0.59–0.79 for drainage discharge and AD = 8.3–16.3 mg l−1; RMSE = 12.4–27.6 mg l−1; R2 = 0.4 for NO3‐N). Based on the scenario analyses, the D0.40L50 drainage system was the best one, resulting in fewer environmental effects from the nitrate‐N and water loss viewpoints. © 2020 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 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".