The effectiveness and resilience of phosphorus management practices in the Lake Simcoe watershed, Ontario, Canada
Bibliographic record
Abstract
Abstract Uncertainty surrounding future climate makes it difficult to have confidence that current nutrient management strategies will remain effective. This study used monitoring and modeling to assess current effectiveness (% phosphorus reduction) and resilience (defined as continued effectiveness under a changing climate) of best management practices (BMPs) within five catchments of the Lake Simcoe watershed, Ontario. The Integrated Catchment Phosphorus model (INCA‐P) was used, and monitoring data were used to calibrate and validate a series of management scenarios. To assess current BMP effectiveness, models were run over a baseline period 1985–2014 with and without management scenarios. Climate simulations were run (2070–2099), and BMP resilience was calculated as the percent change in effectiveness between the baseline and future period. Results demonstrated that livestock removal from water courses was the most effective BMP, while manure storage adjustments were the least. Effectiveness varied between catchments, influenced by the dominant hydrological and nutrient transport pathways. Resilience of individual BMPs was associated with catchment sensitivity to climate change. BMPs were most resilient in catchments with high soil water storage capacity and small projected changes in frozen‐water availability and in soil moisture deficits. Conversely, BMPs were less resilient in catchments with larger changes in spring melt magnitude and in overland flow proportions. Results indicated that BMPs implemented are not always those most suited to catchment flow pathways, and a more site‐specific approach would enhance prospects for maintaining P reduction targets. Furthermore, BMP resilience to climate change can be predicted from catchment physical properties and present‐day hydrochemical sensitivity to climate forcing.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".