AN INTEGRATED INFRASTRUCTURE PRIORITIZATION MODEL: CASE STUDY OF TRIPOLI, LEBANON
Bibliographic record
Abstract
This paper introduces a novel non-linear weighting model to evaluate the different aspects of infrastructure. In this regard, three approaches are presented to compute the percents of infrastructure indicators, namely analytical hierarchy process, Shannon entropy and fuzzy set theory. A unified weighting model is then proposed to aggregate the weighting vectors obtained from the three approaches. The developed model is designed to model the feedback of the experts, actual condition of the infrastructure and encountered uncertainties. Results highlighted that water has the highest relative importance with 52.65% followed by electricity with 34.12% while telecommunication has the least relative importance with 2.69%. To apply a more practical sense to the proposed framework, a sample assessment of the Lebanese city of Tripoli’s civil infrastructure was carried out in this paper. The developed model is expected to support planners and policymakers with a platform that enables them to efficiently evaluate the infrastructure’s condition
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".