{"id":"W7162026245","doi":"10.82308/50120","title":"ArcGIS inventory analysis for risk assessment of road tunnels","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Underground infrastructure and sustainability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Risk assessment; Hazard; Vulnerability (computing); Vulnerability assessment; Natural hazard; Sampling (signal processing); Geographic information system; Hazard analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006804743,0.0009006956,0.0003171237,0.004929008,0.0003150147,0.001322965,0.0007147619,0.000235407,0.008479444],"category_scores_gemma":[0.002096515,0.0004034654,0.0007765231,0.004930573,0.0001541466,0.0007598074,0.0006591584,0.0004263659,0.002066636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001101073,"about_ca_system_score_gemma":0.002264012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06123788,"about_ca_topic_score_gemma":0.05111755,"domain_scores_codex":[0.9995179,0.00006301523,0.0000514488,0.00008715405,0.0002234433,0.0000570782],"domain_scores_gemma":[0.9992265,0.0001189104,0.0000981847,0.0000969289,0.0004275953,0.00003193835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002871168,0.00032403,0.09970956,0.0007640103,0.0003696059,0.0006522368,0.0009681087,0.3729905,0.005664724,0.02196531,0.09692316,0.3993816],"study_design_scores_gemma":[0.00005477114,0.00007339554,0.100001,0.0001390977,0.0001209662,0.0003218015,0.001481039,0.7410409,0.008702299,0.008778137,0.1391789,0.0001077639],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2716327,0.0004780656,0.4482872,0.0003896746,0.0001721714,0.001801204,0.2015351,0.03159213,0.04411167],"genre_scores_gemma":[0.5136228,0.000556985,0.3327175,0.00004869244,0.00002720282,0.001473032,0.1402774,0.001443522,0.009832842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06123788,"threshold_uncertainty_score":0.1217629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009715190057832007,"score_gpt":0.2827535263544572,"score_spread":0.2730383362966252,"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."}}