{"id":"W4412754671","doi":"10.11159/iccste25.342","title":"Vulnerability and Recovery Co-Analysis to Enhance Resilience of Ports Impacted By Extreme Weather Events - Preliminary Results from EU Project Safari","year":2025,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Civil, Structural and Transportation Engineering","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Resilience (materials science); Vulnerability (computing); Extreme weather; Vulnerability assessment; Computer science; Environmental resource management; Environmental science; Computer security; Psychological resilience; Climate change; Geology; Oceanography; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002316245,0.0008515092,0.0005247121,0.002271641,0.0003705148,0.0009969634,0.0004730686,0.0005783323,0.002323445],"category_scores_gemma":[0.003599929,0.0001897701,0.001131439,0.001364803,0.0006137213,0.001290302,0.001288216,0.0006083127,0.0002430822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001233451,"about_ca_system_score_gemma":0.0006827169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008252394,"about_ca_topic_score_gemma":0.005078849,"domain_scores_codex":[0.9991505,0.0003998942,0.00003297696,0.0001042795,0.0001760544,0.0001362128],"domain_scores_gemma":[0.99731,0.001456946,0.000323314,0.000279133,0.0004204818,0.0002100732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001060454,0.0007639806,0.0697555,0.0001569455,0.0003326365,0.0007470837,0.0005362791,0.8389498,0.006168386,0.009884187,0.001777596,0.06986717],"study_design_scores_gemma":[0.00003106212,0.0004153328,0.04621276,0.00002331974,0.000108345,0.0001477607,0.0007486515,0.9402215,0.005131834,0.005437633,0.001484562,0.00003727184],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9575813,0.0001690637,0.03656347,0.0003364253,0.00001967188,0.0001164334,0.0006589469,0.0003500983,0.004204565],"genre_scores_gemma":[0.986021,0.0001043403,0.01188292,0.00001359965,0.000009768702,0.00005682061,0.0006189459,0.00004300653,0.001249604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008252394,"threshold_uncertainty_score":0.01640868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009640612540466413,"score_gpt":0.2538966187481419,"score_spread":0.2442560062076755,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}