Estimation of spring runoff phosphorus loading for an event-based BMP spatial targeting model in an ungauged Canadian Prairie basin in Manitoba
Bibliographic record
Abstract
Beneficial Management Practices (BMPs) are widely recognized as a prominent strategy in the Canadian Prairies to reduce phosphorus loading and investments for BMPs are rapidly increasing. Event-based spatial targeting models like the Prioritize Target and Measure Application (PTMApp) can be used to better inform prospective investments, however, the unique characteristics of Canadian Prairie hydrology need consideration if BMPs are to be prioritized for maximum benefits. It is argued that assessing high-volume, high-frequency events like spring runoff when targeting BMPs in Canadian Prairie basins should be considered a best practice when developing PTMApp models. This research paper outlines the approach used for developing and evaluating PTMAPP model inputs for spring runoff in an ungauged basin on the Canadian prairies, in Southern Manitoba. Surrogate models neighbouring the study area were created to generate transferable model parameters that inform water quantity (HEC-HMS) and quality (PTMApp) models. Frequency and regression analyses of discharge and total phosphorus were also performed to help define the magnitudes of these events and to apply bias corrections. Given further advancements in data, methods, and insights, BMP spatial targeting should become increasingly normalized within the Canadian Prairies in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".