Land Based Solutions to Eutrophication -Exploring the use of ACPF in a Canadian context
Bibliographic record
Abstract
Agricultural conservation measures (ACM’s) are actions taken at the farm scale with the intention of maintaining agricultural production while simultaneously reducing soil and nutrient runoff into freshwater ecosystems. In the Great Lakes basin, they are an essential land-based tool for addressing eutrophication and harmful algal blooms downstream. In these contexts, large reductions in watershed-level nutrient loss have the potential be achieved, through the coordinated placement of ACM’s, resulting in improved surface water quality at relatively low economic cost. The Agricultural Conservation Planning Framework (ACPF) is a decision-support tool designed to assist conservation managers improve freshwater quality by optimizing ACM placement at the watershed scale. Using a case study approach, we adapted the ACPF GIS Toolbox for watersheds in Essex County, Ontario that feed into the western basin of Lake Erie, a hotspot of eutrophication and harmful algal blooms. Here we present the first adaption of the ACPF toolbox for a Canadian watershed. To investigate the utility for real-world application by conservation practitioners, we compared two agriculturally-dominated watersheds: River Canard (less forested) and the Cedar Creek (more forested) both in Essex County. Geospatial statistics and multivariate analysis provide additional insight into whether watershed scale conservation actions are being effectively optimized to maximise freshwater health improvements. Results from this effort suggest that mismatches in the implementation of ACMs across a watershed exist and are a factor in disappointing freshwater restoration outcomes. We provide actionable recommendations for research, practitioners and decision-makers to help advance further application of this tool in SW Ontario.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".