Modelling nitrogen transport through vegetative filter strips in Ontario condition
Bibliographic record
Abstract
Surface runoff is the pathway of nitrogen transport from agricultural land to water bodies. Vegetative filter strips are one of the best management practices, and are considered to be effective in improving water quality. The objective of this study was to investigate the impact ofSchool of Rural Planning and Development. Experimental data were collected at two sites in Ontario to evaluate the Grass Filter Strip Model (GFSM). The GFSM is a field scale, continuous model which can also be used for storm events. The GFSM is used to describe N transport and dynamics in vegetative filter strips. GFSM handles sediment, nitrate, sediment-bound and dissolved ammonium, and sediment-bound organic N transport during a runoff events. The model also incorporates daily percolation, transpiration, the dynamics of nitrate, sediment-bound and dissolved ammonium and sediment-bound organic N in the filter strip between runoff events. Field experiments were conducted in the summer of 2002 at the Guelph Turf Grass Institute, Guelph, Ontario with filter strip lengths of 5, 10, and 20 m and flow rates of 0.5, 1, and 1.5 L/s. In the fall of 2002, more experiments were conducted at the Elora Research Station (near Guelph) with a 10-metre filter strip planted with Kentucky blue-grass and the same flow-rates used as in the first experiment. In both experiments, a mixture of dairy manure, soil and water was introduced at the inlet and water samples were collected at the outlet for analysis of dissolved ammonium and nitrate. Experimental results showed that vegetative filter strips are effective in removing 87 and 82% of nitrate and dissolved ammonium, respectively. The GSFM performed reasonably well in simulating runoff through vegetative filter strip, but for simulating nitrate and dissolved ammonium, it is comparatively less efficient.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".