Comparison of GIS‐Based Logic Scoring of Preference and Multicriteria Evaluation Methods: Urban Land Use Suitability
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
Multi‐criteria evaluation (MCE) methods are useful tools to evaluate the land suitability for various uses and assist in the effective management of available land. Many common GIS‐based MCE methods, such as analytical hierarchy process (AHP), ordered weighted averaging (OWA), and a combination of AHP and OWA methods (AHP–OWA) are not able to fully represent all the logic that constitute a wide range of human decision‐making reasoning. Consequently, improved GIS‐based MCE methods such as Logic Scoring of Preference (LSP) method are needed. The main objectives of this study are to: (1) implement the GIS‐based LSP method for land suitability evaluation and (2) compare qualitatively and quantitatively the suitability maps generated by LSP and three GIS‐based MCE methods. This study was implemented with data sets from Boulder County, Colorado, USA for the case study of the urban land suitability evaluation. The qualitative properties of MCE methods and the Receiver Operating Characteristic (ROC) statistics were used as comparison metrics. The results indicate that soft computing methods and particularly LSP performed the best among GIS‐based MCE methods for the urban land use application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it