Calibration and Evaluation of Phosphorus Loss in Surface Runoff and Subsurface Drainage Using APEX
Bibliographic record
Abstract
Modeling phosphorus (P) loss through surface runoff and subsurface drainage is essential because it helps understand how P transfers to the water bodies in an inexpensive and feasible way. P loss into the Great Lakes leads to eutrophication. APEX (Agriculture Policy/Environmental eXtender) is extended from EPIC (Environmental Policy Integrated Climate model) and can simulate management practices and land use impacts for various land sizes from a field to a small watershed. However, APEX has not been tested in Lake Erie Region. This research, therefore, represents the first effort to use APEX to simulate P loss in this area. Field data were obtained from experiments conducted at the Agriculture and Agri-Food Canada's Whelan experimental farm in Woodslee, ON, Canada, with corn-soybean rotation. Calibration and evaluation of APEX was executed to test its capability in simulating the impacts of chemical fertilizers and cattle manure on P loss. Different potential evapotranspiration equations (PET) and curve number (CN) equations were used to determine the most suitable one for this study area. Statistical analysis was used to assess the model performance. Satisfactory results were obtained from the simulation of APEX in the Brookstone clay loam soil.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".