{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001717107,0.0008634659,0.001040245,0.001882683,0.0005828633,0.001259393,0.001409828,0.0009265265,0.00678503],"category_scores_gemma":[0.002893604,0.0005759304,0.0008266904,0.0008048759,0.0002388189,0.001432526,0.001665695,0.0006928496,0.002106099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005104209,"about_ca_system_score_gemma":0.001196626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00241337,"about_ca_topic_score_gemma":0.002541225,"domain_scores_codex":[0.9989102,0.0002145754,0.0001468829,0.0002620742,0.0004029162,0.00006340091],"domain_scores_gemma":[0.9988851,0.0004017287,0.00008289328,0.0001841214,0.0003860701,0.00006018416],"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":[0.0006618208,0.0008163663,0.007969071,0.0008523245,0.0002696744,0.001971499,0.002225294,0.1170993,0.0799607,0.00800163,0.01917815,0.7609942],"study_design_scores_gemma":[0.0001647919,0.0001116903,0.003759016,0.0001354793,0.0001970399,0.0006715799,0.0004568814,0.9387782,0.02395351,0.005652676,0.02602172,0.00009756646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03668017,0.0002166913,0.9335202,0.0001782722,0.00003501415,0.0004678514,0.0006632495,0.02096422,0.007274308],"genre_scores_gemma":[0.2905894,0.0003463951,0.6989095,0.0001300625,0.00003190461,0.0005916029,0.002470315,0.000390692,0.006540012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00678503,"threshold_uncertainty_score":0.02269822,"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."}}