The Effect of Climate Change on Land Use and Wetlands Conservation in Western Canada: An Application of Positive Mathematical Programming
Bibliographic record
Abstract
This study examines the impact of climate change on land use in the Prairie Pothole Region of Western Canada, with particular emphasis on how climate change will impact wetlands. A multi-region Positive Mathematical Programming model calibrates land use in the area to observed acreage in 2006. Policy simulations for both climate effects as well as the effects of biofuel policies determine how climate change will affect land use and wetlands. Given that the model calibrates to observed acreage, the policies provide a realistic view of how land use might change from current levels, given the effects of climate change. Results indicate that climate change could decrease wetlands in this area by as much as 50 percent. The effect will be very different depending on whether or not the social benefits of wetlands are considered, and the effects of climate change on wetlands are heterogeneous across the Prairie Provinces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".