An Analytical Hierarchical Process-based decision-making approach for selecting car-sharing stations in medium size agglomerations
Bibliographic record
Abstract
This article presents an Analytical Hierarchical Process (AHP)-based multi-step approach for identifying car-sharing stations in medium size agglomerations. Each agglomeration consists of several communities (also called communities). The first step consists of identifying communities that contain enough population that would be interested in using car-sharing. In the second step, we identify the potential locations for car-sharing stations inside the communities. In the third step, experts rate the stations using several criteria. AHP is used to compute the criteria and station weights. The individual weightings of the criteria and the stations are used to compute overall weights of the stations with respect to all the criteria. These weighted stations are then subjected to a predefined threshold. All those stations whose overall weight exceeds the threshold limit are considered as final stations for car-sharing implementation. We validate our approach by application on various communities of Poitou-Charentes region.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".