Modeling Phosphorus Losses through Surface Runoff and Subsurface Drainage Using ICECREAM
Bibliographic record
Abstract
Modeling soil phosphorus (P) losses by surface and subsurface flow pathways is essential in developing successful strategies for P pollution control. We used the ICECREAM model to simultaneously simulate P losses in surface and subsurface flow, as well as to assess effectiveness of field practices in reducing P losses. Monitoring data from a mineral‐P‐fertilized clay loam field in southwestern Ontario, Canada, were used for calibration and validation. After careful adjustment of model parameters, ICECREAM was shown to satisfactorily simulate all major processes of surface and subsurface P losses. When the calibrated model was used to assess tillage and fertilizer management scenarios, results point to a 10% reduction in total P losses by shifting autumn tillage to spring, and a 25.4% reduction in total P losses by injecting fertilizer rather than broadcasting. Although the ICECREAM model was effective in simulating surface and subsurface P losses when thoroughly calibrated, further testing is needed to confirm these results with manure P application. As illustrated here, successful use of simulation models requires careful verification of model routines and comprehensive calibration to ensure that site‐specific processes are accurately represented. Core Ideas The ICECREAM model's first trial simulating P losses in both runoff and drainage. ICECREAM satisfactorily simulated tile drainage and crop P uptake. The model adequately simulated total P loss and its response to P fertilization. Exceptional care should be taken in simulating particulate P under high drainage flow. Spring tillage and fertilizer injection were recommended to reduce P loss.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".