Innovative MADM Framework for Strengthening Port Resilience Against Extreme Weather
Bibliographic record
Abstract
The increasing frequency and intensity of extreme weather events pose a significant threat to global port infrastructure, disrupting operations, causing economic losses, and compromising the resilience of maritime trade networks.This study aims to address this issue by introducing the Infrastructure Assessment Tool, a decision-making framework based on Multiple Attribute Decision Making methodology, designed to systematically evaluate multiple, often conflicting, criteria in infrastructure assessments.The tool facilitates a comprehensive evaluation of port resilience by considering seven key criteria: structural integrity, road conditions, equipment availability, drainage systems, supporting building conditions, energy systems, and access routes.Data for the tool were collected through a combination of visual inspections, direct measurements, historical record analysis, and expert opinions, which were subsequently converted into a standardized Likert scale for comparative assessment.The Simple Additive Weighting method was utilized to assign relative importance to each criterion, incorporating expert judgment into the evaluation process.The tool's effectiveness was demonstrated through a case study at Tarakan Port, Indonesia.Its flexibility and comprehensiveness enable port managers to proactively assess vulnerabilities, prioritize interventions, and implement targeted measures to strengthen resilience against extreme weather.Consequently, it contributes to ensuring the sustainability and continuous operation of ports, safeguarding their essential role in supporting global trade and economic growth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".