{"id":"W4390979331","doi":"10.36371/port.2023.special.5","title":"Risk and Emergency Management System to Mitigate Disasters","year":2024,"lang":"en","type":"article","venue":"Journal Port Science Research","topic":"Knowledge Management and Technology","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Emergency management; Government (linguistics); Geographic information system; Risk management; Environmental planning; Business; Landslide; Risk analysis (engineering); Disaster response; Environmental resource management; Geography; Computer security; Computer science; Engineering; Political science; Remote sensing; Environmental science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.03891634,0.0001232122,0.0001908128,0.00449544,0.001363273,0.002090718,0.002137915,0.00004352066,0.0002308822],"category_scores_gemma":[0.001340992,0.000079956,0.00008103113,0.008620424,0.0007167954,0.0008177452,0.001672752,0.0006475665,0.002113161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000231224,"about_ca_system_score_gemma":0.0001340276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001224608,"about_ca_topic_score_gemma":0.00001908924,"domain_scores_codex":[0.9928482,0.0002266567,0.0006602188,0.0008021304,0.004571303,0.0008914908],"domain_scores_gemma":[0.9978942,0.0002337757,0.00008367813,0.0005870698,0.0006414378,0.0005597849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001381843,0.00002655277,0.01688639,0.00005268116,0.00002912868,0.0009173633,0.0008259534,0.00002418607,0.0003630085,0.07313106,0.04341419,0.8643157],"study_design_scores_gemma":[0.0003381763,0.0005954374,0.08682697,0.0004542389,0.00004496225,0.0006836848,0.04200662,0.01995998,0.0003628013,0.1680551,0.6801251,0.0005469273],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8661943,0.001078231,0.01425032,0.005493791,0.002986425,0.0005082876,0.000003959955,0.0001233659,0.1093613],"genre_scores_gemma":[0.9738483,0.0003263686,0.0006902331,0.00001033086,0.0001668059,0.00001543965,8.771713e-8,0.00000974025,0.02493268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8637688,"threshold_uncertainty_score":0.9999368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1449991079264008,"score_gpt":0.4875143046561793,"score_spread":0.3425151967297785,"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."}}