Bibliographic record
Abstract
This paper develops an integrated economic, hydrologic and GIS modeling framework to examine the cost‐effective targeting of land retirement for establishing riparian buffers in agricultural watersheds. Previous studies have examined the efficiency of targeting large land parcels for retirement or targeting management practices such as conservation tillage but have not considered narrow variable buffer strips. An empirical application of the framework in the Canagagigue Creek watershed in Ontario shows that average and marginal costs of sediment abatement increase at an increasing rate as the environmental goal becomes more stringent. The locations of the buffer strips vary across the watershed and are not necessarily located on those sites with greatest slope or those adjacent to visible streams. Cost effectiveness is further increased if the targeting is extended to allow for the width of the buffer strip to vary by location rather than assume a uniform width. The modeling results have important policy implications for the design of conservation stewardship programs such as setting appropriate environmental health goals based on marginal abatement costs relative to marginal benefits, and setting physical characteristics of the riparian buffers for selection along the drainage network in targeted sub‐catchments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".