Evaluation of the Effect of Cell Size on the Performance of AGNPS Model
Bibliographic record
Abstract
Models have become an important tool to select nonpoint source pollution managementstrategies. The Agricultural Non-Point Source Pollution model (AGNPS) was used to evaluatethe effect of cell size on the estimation of pollutant loads in the Canagagigue Creek watershed ofthe Grand River, Ontario. The GIS interface of the model (RAISON) was used to extract inputparameters from digital elevation model, soils and land use layers. The cell size used was 25, 50,100, 150, 300, and 500 ha with storm return period of 2,5,10, and 25 years. An interesting finding of the study was that the size of cells in the GIS model has a significanteffect on the model predictions. The runoff and sediment yield predicted by the model observedat the sub-watershed outlet and the watershed outlet showed erratic pattern with an increase incell size. There was increase in estimated runoff volume and sediment yields when cell sizeincreased from 25 ha to 100 ha, again increase in cell size from 100 to 150 ha showed decreasein runoff volume and sediment yield. Further increase in cell size greater than 150 ha showed anincrease in runoff volume and sediment yield. Particular care must be dedicated to selection ofcell size and its effect on the AGNPS model results.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".