{"id":"W1456821793","doi":"10.22260/isarc2013/0163","title":"Risk Identification Expert System for Metro Construction Based on BIM","year":2013,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"BIM and Construction Integration","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Knowledge base; Expert system; Tacit knowledge; Domain (mathematical analysis); Computer science; Risk analysis (engineering); Engineering; Building information modeling; Risk management; Knowledge extraction; Bridge (graph theory); Knowledge management; Data mining; Artificial intelligence; Operations management","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":[0.0001327298,0.0001010181,0.0001077343,0.0001017753,0.0001008813,0.00006340196,0.0001447043,0.00007030492,0.00002177963],"category_scores_gemma":[0.00006271496,0.00007521966,0.00008994274,0.0001777556,0.00005037189,0.0002071254,0.00000743472,0.00008610442,0.0000180703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009342632,"about_ca_system_score_gemma":0.000007060229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002923466,"about_ca_topic_score_gemma":9.037753e-7,"domain_scores_codex":[0.9993631,0.00000382372,0.0002441335,0.0001251913,0.0001545962,0.000109197],"domain_scores_gemma":[0.999436,0.00003135202,0.0001395708,0.00009247407,0.0002742245,0.00002643437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009616855,0.00007005336,0.01811015,0.001265531,0.0001802184,2.258333e-8,0.0006547409,0.003231614,0.6558988,0.1438098,0.02148027,0.1552026],"study_design_scores_gemma":[0.0005326926,0.00005139461,0.0048105,0.0001752538,0.0000535786,0.000005427211,0.00315488,0.2769798,0.7092056,0.003462857,0.001361637,0.000206475],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959094,0.00009413662,0.0258952,0.0004363557,0.002617226,0.001271199,0.00003495847,0.0005080481,0.01004887],"genre_scores_gemma":[0.9957922,0.000005654515,0.003750322,0.00001596311,0.0001172779,0.0002506895,0.000002061815,0.00001803804,0.00004776746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2737482,"threshold_uncertainty_score":0.3067369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007213105801386763,"score_gpt":0.194074947907399,"score_spread":0.1868618421060122,"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."}}