{"id":"W2892141854","doi":"10.2495/safe-v8-n4-515-527","title":"Adaptation investments for transport resilience: trends and recommendations","year":2018,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Leverhulme Trust","keywords":"Vulnerability (computing); Investment (military); Flood myth; Flooding (psychology); Resilience (materials science); Business; Climate change; Adaptation (eye); Transport network; Environmental resource management; Natural resource economics; Computer science; Environmental science; Transport engineering; Geography; Economics; Computer security; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002220478,0.00008165222,0.0001252343,0.0002270488,0.00004943542,0.00002363052,0.00009506612,0.00004510591,0.00002736352],"category_scores_gemma":[0.00004247938,0.00007862419,0.00004956187,0.0001002235,0.00004159676,0.0003225103,0.000007690305,0.0001085375,2.330984e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004188377,"about_ca_system_score_gemma":0.0000108216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005262563,"about_ca_topic_score_gemma":0.0000201659,"domain_scores_codex":[0.9993886,0.000006419101,0.0003004694,0.00007278133,0.0001404233,0.00009129254],"domain_scores_gemma":[0.9996201,0.00005764324,0.00005126835,0.00003979935,0.0001692417,0.0000619096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004746935,0.00007652251,0.003831235,0.0001801739,0.001154518,0.00001698072,0.01249561,0.5374302,0.004942568,0.02094004,0.0005807942,0.4178767],"study_design_scores_gemma":[0.001129404,0.0001751766,0.02577036,0.0001457324,0.00008856789,0.0001286349,0.0003936129,0.9305703,0.001649698,0.002835793,0.03685656,0.0002561402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3879684,0.0006648771,0.6076794,0.001578376,0.001371527,0.00008708283,0.00007877346,0.00005120923,0.0005203687],"genre_scores_gemma":[0.9936067,0.0006247314,0.005431066,0.00003396157,0.0002711178,0.000001530705,0.00001222887,0.00000710878,0.00001156691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6056383,"threshold_uncertainty_score":0.3206201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006650977711275635,"score_gpt":0.2460469905804967,"score_spread":0.2393960128692211,"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."}}