Spatially-Explicit, Exposure-Based Assessment of Surface Water Vulnerability from Land Use Threats for Time-Efficient and Cost-Effective Watershed Development Planning
Bibliographic record
Abstract
The utility of a spatially-explicit, exposure-based model was examined for its suitability as a tool for rapidly assessing surface water vulnerability in watershed planning. This simple GIS-model uses three types of easily obtainable spatial information: (1) sources of land use-induced change; (2) intensity of watershed drainage; and (3) sensitivity of drainage basins to change. This model was applied to the Thomas Brook watershed in Nova Scotia, Canada, which has been the site of previous studies, conducted over multiple years, using detailed, effects-based, hydrologic models. Doing so allowed us the opportunity to compare the two approaches. Results showed a good concordance in the derived mapped outputs between the two models. Given the rapid ease and inexpensive cost of using the GIS, exposure-based model, we believe it to offer great promise in terms of prioritizing locations for further study or for intervention of best management practices, as well as for planning where to best direct future water-sensitive development through build-out analyses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".