Multiple-Criteria Sorting Using Case-Based Distance Models With an Application in Water Resources Management
Why this work is in the frame
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Bibliographic record
Abstract
A case-based distance model to solve sorting problems in multiple-criteria decision analysis (MCDA) is developed, and its application in water resources management is presented. The sorting problem in MCDA is to arrange a set of alternatives into ordered groups. MCDA is introduced as consequence-based preference aggregation, whereby consequence and preference expressions (values and weights) are defined and combined in a sequence of steps. Then, sorting problems are defined, and some properties are explained. Based on weighted Euclidean distance, two case-based distance models are developed for sorting using weights and group thresholds obtained by assessment of a case set provided by a decision maker (DM). This case-based method can elicit the DM's preferences more expeditiously and accurately than direct inquiry. Case-based sorting model I is designed for cardinal criteria, while its extension, i.e., case-based sorting model II, can handle both cardinal and ordinal criteria. Optimization programs are employed to find the most descriptive weights and group thresholds. A case study in which Canadian municipalities are sorted according to water usage is presented.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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