Effects of Crop Prices, Nuisance Costs, and Wetland Regulation on Saskatchewan NAWMP Implementation Goals
Bibliographic record
Abstract
Current Saskatchewan wetland regulation may be insufficient to meet North American Waterfowl Management Plan (NAWMP) preservation and restoration goals in a climate of increasing demand for grains and nuisance costs. Therefore, the purpose of this paper is to ascertain the effects that crop prices, nuisance costs, and alternate wetland regulation have on these goals. An integrated geographic information system and economic farm‐level model that assesses the net present value of drainage projects in the Whitesand River Watershed is employed. If prices eventually reach historical highs observed in the early 1970s, more than 85% of the wetland area in the study area could be drained, making NAWMP goals impossible to achieve. In this scenario, nuisance costs have little effect on drainage outcomes because they are dwarfed by the magnitude of agricultural revenue. If prices remain at the current higher levels observed from 2007 to 2012, the use of a binding permit could help achieve NAWMP goals. In this case, nuisance costs play a large role in determining the drainage of marginal, comparatively larger wetlands. If prices return to the recent lower levels observed from 1999 to 2006, current Saskatchewan regulation is sufficient. In this scenario, agricultural returns are low and nuisance costs are not high enough to cause wetland drainage. Both wetland regulation and nuisance costs can play an important role in agricultural wetland drainage, but that role depends critically upon the price of agricultural products.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".