Identifying critical source areas of phosphorus from selected small, agricultural watersheds of Ontario, Canada using different models
Bibliographic record
Abstract
Resurgence of algal blooms in the Great Lakes can be linked to various factors such as increased phosphorus loading from agricultural areas, changing climate, increased urbanization, etc. Among others, reducing phosphorus loading from agricultural areas offers a huge potential. As such, plausible agricultural best management practices (BMPs) need to be formulated, tested and implemented. Often, such BMPs are randomly selected and placed, thereby making them economically unfeasible and unattractive. Therefore, there is a pressing need to identify and test targeted placement and selection of the BMPs. While a range of watershed modelling tools is available which can simulate phosphorus dynamics from upstream watershed to a receiving water body, a systematic evaluation of suitability of these tools in identifying critical source areas (CSAs) for phosphorus loading, is still lacking. Hence, in this study, we will evaluate the suitability of the Soil and Water Assessment Tool (SWAT), the Annualized Agricultural Non-Point Source (AnnAGNPS), and the Guelph model for evaluating effects of Agricultural Management systems on Erosion and Sedimentation (GAMES), explicitly for this purpose. These modelling tool will be tested in several small, agricultural watersheds of the Great Lakes region. As such, spatiotemporal variability of phosphorus loading will be analyzed in view of locating the CSAs. Furthermore, various BMPs will be placed at those target areas and their effectiveness in reducing the phosphorus loading will be evaluated.
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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 | 0.001 | 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) | 0.002 | 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".