Constructing resilience model of port infrastructure based on system dynamics
Bibliographic record
Abstract
The port industry, which plays an important role in Korea's economy, is exposed to various disasters such as earthquakes, tsunamis, and chemical accidents.Therefore, resilience needs to be assessed to evaluate how properly port system can recover its function even after being damaged, and weak points should be complemented by the policy.However, the port infrastructure is too complicated to analyze all the components, so a systemic approach is needed.Therefore, this study evaluates the resilience of the port infrastructure using system dynamics model, which can compare quantitative performance index.This study sets up the cargo process, the most important economic index of the port, as the performance level and constructs a system dynamics model by finding elements corresponding to attributes of resilience.In addition to disruption and recovery actions in the disaster situation, the model also incorporates socioeconomic factors such as changes in cargo demand and financial state, resulting in close proximity to case studies.Simulation of disaster situations with resilience assessment model can express recovery process of the system and accumulated economic damage.By applying various inputs and scenarios, the result of this study can be used as a basis for comparing the resilience of port infrastructure and establishing the reinforcement policy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".