Assessing water and nitrate‐N losses from subsurface‐drained paddy lands by DRAINMOD‐N II
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 NO 3 − ‐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 ( R 2 ). The simulated and observed drainage discharges (0.97 vs 0.96 mm day −1 ) and NO 3 ‐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 ; R 2 = 0.59–0.79 for drainage discharge and AD = 8.3–16.3 mg l −1 ; RMSE = 12.4–27.6 mg l −1 ; R 2 = 0.4 for NO 3 ‐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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 it