Spatially explicit modeling of wetland conservation costs in Canadian agricultural landscapes
Bibliographic record
Abstract
Abstract Agriculture is an important source of food, employment, and tax revenue to society. However, agricultural expansion is an important driver of global natural ecosystem degradation, including wetlands. Economic theory shows that wetland loss is caused by a mismatch between the private wetland conservation costs borne by landowners and the public benefits generated. We develop a spatially explicit wetland management model to estimate the private economic benefit of wetland drainage in an agricultural landscape in Alberta, Canada. We estimate a full wetland supply curve and show that the private economic benefits of wetland drainage are highly heterogeneous within a watershed. We then combine these private costs of wetland conservation with non‐monetary measures of public ecosystem benefits to assess four wetland conservation policy targeting scenarios. We find a positive correlation between the opportunity cost of wetland conservation on private landowners and the amount of environmental benefits wetlands offer, suggesting that conserving the wetlands that impose the lowest opportunity cost may not be optimal targets for wetland conservation policy. We contribute to wetland conservation economics by demonstrating that targeted wetland conservation policies can be more effective than a uniform conservation policy that assumes wetlands within agricultural landscapes have the same costs and benefits.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".