{"id":"W2065612362","doi":"10.1115/ipc2002-27103","title":"Natural Hazard Database Application: A Tool for Pipeline Decision Makers","year":2002,"lang":"en","type":"article","venue":"4th International Pipeline Conference, Parts A and B","topic":"Geotechnical Engineering and Underground Structures","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Hazard; Work (physics); Database; Computer science; Hazard analysis; Construction engineering; Engineering; Reliability engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000880626,0.0001903878,0.0001721143,0.00009885017,0.00006350859,0.0001042845,0.0001969043,0.0000806977,0.0001925949],"category_scores_gemma":[0.0001086808,0.0001678084,0.00006161776,0.00007681717,0.00003883529,0.0001411412,0.00004160855,0.0001752307,0.00002097414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002556901,"about_ca_system_score_gemma":0.000007009678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005101719,"about_ca_topic_score_gemma":0.00001797223,"domain_scores_codex":[0.9990271,0.000004796252,0.000305062,0.0002551955,0.0002030486,0.0002047803],"domain_scores_gemma":[0.9994404,0.000109964,0.00003335225,0.0001924195,0.0001421494,0.00008166564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001994163,0.0001621182,0.0004862237,0.0002622528,0.0001827089,0.00002042254,0.0001964833,0.06642786,0.007943037,0.08932689,0.264301,0.5704916],"study_design_scores_gemma":[0.0005597488,0.00001372566,0.0002043582,0.00003406198,0.00001226714,0.0000202684,0.000008056239,0.7433945,0.0001734302,0.001548692,0.2538501,0.0001807795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01893185,0.0007299496,0.9738962,0.001005394,0.001218829,0.000328432,0.0001716059,0.0003590263,0.003358723],"genre_scores_gemma":[0.9904836,0.0002206164,0.006551093,0.0001572411,0.0003960071,0.00007575095,0.0001859625,0.00002108678,0.001908574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9715518,"threshold_uncertainty_score":0.6843029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01671016689432572,"score_gpt":0.242414797654694,"score_spread":0.2257046307603683,"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."}}