Simulation of Nitrate‐N Leaching in No‐Till Fields with DRAINMOD‐N II in a Cold‐Humid Region
Bibliographic record
Abstract
Abstract Conservation agriculture, especially no‐tillage, has proven to become sustainable farming in many agricultural environments globally. In spite of advantages of no‐till systems, this practice may result in excess infiltration into the soil and can enhance the movement of mobile nutrients and some pesticides to subsurface drains and groundwater along preferential pathways. The goal of this study was to evaluate the capacity of DRAINMOD‐N II to simulate subsurface nitrate‐N leaching in no‐till fields in Truro, Nova Scotia, Canada, from 2003 to 2006. The model performance was first evaluated by comparing observed and simulated drain outflow data that is an essential prerequisite for the model to obtain a proper prediction of NO3‐N movement, and then by comparing observed and simulated NO3‐N concentration in no‐till fields using three statistical indices, relative root mean square error (RRMSE), average absolute deviation (AAD) and the correlation coefficient (R2). The RRMSE, AAD and R2 for the validation period were determined to be 1.09, 1.85 and 0.83 mm for drain outflow, and 1.43, 0.51 and 0.79 mg l−1 for NO3‐N concentration respectively. The results showed that DRAINMOD‐N II predicted NO3‐N leaching reasonably well in drainage outflow of no‐till fields over the whole period. Copyright © 2018 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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".