Modelling phosphorus loss through surface runoff and tile drainage from agricultural fields in Southern Ontario using ICECREAM
Bibliographic record
Abstract
Phosphorus loss from agricultural fields through water flows has been a serious environmental problem, which leads to water quality issues such as eutrophication.In addressing the problem, firstly, methods and equations related to phosphorus loss through subsurface tile drain in water quality models were reviewed.Eight existing water quality models (ADAPT, ANIMO, APEX, HYDRUS, ICECREAM, MACRO, PLEASE, SWAP) were concluded to be capable to simulate P transport to tile drainage based on their equations.Originated from GLEAMS and CREAMS, while adapted to simulate phosphorus losses in agricultural field under Nordic conditions, ICECREAM could be the most current comprehensive model for P loss through tile drains from agricultural fields based on its equations.The objective of this thesis was to use ICECREAM in simulating phosphorus loss through surface runoff and subsurface drainage in a tile drained agricultural field in southern Ontario.This modelling study used the data from an Agriculture and Agrifood Canada (AAFC) experiment site located near Harrow, Ontario.Measured runoff, drainage, and phosphorus mass in runoff and drainage in plots 5 & 9, treated with regular tile drainage and inorganic P fertilizer, were used to calibrate and validate the model.The simulation periods was 4 cropping years with two corn-soybean rotation, from Jun 1 st , 2008 to May 12 th , 2012.Water and phosphorus data were measured in eighteen periods between this whole simulation duration.Data from the first two cropping years (2008 and 2009) were used for calibration, and the following two cropping years (2010 and 2011) were used for validation.Simulated runoff, drainage, soluble reactive phosphorus (DRP) and particulate phosphorus (PP) in runoff and drainage were compared with observed data using statistical factors of Nash-Sutcliffe model efficiency (NSE) and percent bias (PBIAS).The results showed that, although ICECREAM failed to simulate TP and PP loss in subsurface drainage in 2010 and 2011, the model generally provided
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".