GIS–Multicriteria Evaluation with Ordered Weighted Averaging (OWA): Case Study of Developing Watershed Management Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper focuses on the parameterized-ordered weighted averaging (OWA) method. OWA is a family of multicriteria evaluation (or combination) rules. The proposed approach uses a parameter that serves as a mechanism for guiding multicriteria evaluation procedures. The parameter is incorporated into a method for obtaining the optimal order weights and for developing a transformation function. The function provides us with a consistent way of modifying the criterion values so that the multicriteria combination procedures can be guided by specifying a single parameter. The parameterized-OWA method has been implemented in a GIS environment as a GIS–OWA module and it has been tested in a real-world situation for developing management strategies in the Cedar Creek watershed in Ontario, Canada. Given a set of evaluation criteria, the problem is to evaluate areas in the watershed for rehabilitation and enhancement projects. Using the GIS–OWA method, a number of alternative strategies for rehabilitation and enhancement projects have been generated and evaluated.
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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 | 0.002 | 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 it