Verification of DRAINMOD ver. 5.1 for estimating water balance and nitrogen transport through soils in southern Ontario
Bibliographic record
Abstract
The overall goal of this research was to determine the effectiveness of DRAINMOD ver. 5.1 to estimate the water balance and flow of nitrogen through an agricultural soil system at the Elora Research Station (ERS) near Elora, Canada. The process compared field hydrological data to the modeled outputs of DRAINMOD for calibration of evapotranspiration input parameters and then compared the modeled nitrogen output to field-measured nitrogen data for two different manure application strategies; fall only and spring only incorporated application. Tile drainage was the main focus of the comparison and total loss estimates in the spring had low errors; however, misplaced timing of the modeled losses contributed to increased errors. Depth to water table (DTWT) estimates were poor during the summer and fall months, contributing to an under-estimate of tile drainage in fall. Modeled tile drain nitrate loss estimates followed the same pattern as the measured tile drainage losses. The results of this research indicate that more research and testing associated with DTWT is required before DRAINMOD ver. 5.1 is a reliable hydrological and nitrogen transport model for southern Ontario.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".