Land-use based modeling approach for determining freshwater nitrate loadings from small agricultural watersheds
Bibliographic record
Abstract
Abstract Nitrate released from a variety of land-use activities is a major factor in the degrading conditions observed in many watersheds and estuaries. In this research a spatially lumped model is developed to estimate annual nitrate loads and concentrations from over 100 small watersheds in the Canadian province of Prince Edward Island (PEI). Nitrate source concentrations are associated with major land-use categories, and nitrate attenuation, based on the width of riparian zones, and transport delay due to groundwater residence time are simulated. To investigate the uncertainty of the results, model parameters were selected using a Latin hypercube sampling method. Nitrate concentrations from 12 watersheds were used for model calibration (R2 = 0.91), while 118 other watersheds were used for verification purposes (R2 = 0.82). Overall, the lumped parameter model is shown to be a useful tool for simulating annual nitrate loadings from agricultural watersheds when detailed spatiotemporal agricultural land-use data are available. For PEI the model results indicate that nitrate loadings to estuaries are strongly related to agricultural land, especially the land area in potato production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".