{"id":"W3197682149","doi":"10.17762/de.vi.3550","title":"Factors Affecting Selection of Excavation Support System","year":2021,"lang":"en","type":"article","venue":"Design Engineering","topic":"BIM and Construction Integration","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Schedule; Selection (genetic algorithm); Decision support system; Excavation; Risk analysis (engineering); Set (abstract data type); Process (computing); Computer science; Operations research; Engineering; Business; Data mining; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.004355797,0.0004209404,0.0002874194,0.002364947,0.001185411,0.002634834,0.0004277615,0.0008242245,0.005896254],"category_scores_gemma":[0.02901949,0.0002503127,0.0003280675,0.001701272,0.0005744214,0.001407218,0.0007911692,0.0004716219,0.001097336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008982517,"about_ca_system_score_gemma":0.001540053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002505791,"about_ca_topic_score_gemma":0.00358205,"domain_scores_codex":[0.9934435,0.002250982,0.0008283405,0.0003772923,0.002286589,0.0008133756],"domain_scores_gemma":[0.9706479,0.02059106,0.003013736,0.0003809401,0.004088168,0.0012782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001215356,0.000624786,0.626116,0.002415022,0.000212193,0.00523697,0.0108763,0.007350971,0.02856725,0.004527662,0.004665642,0.3081918],"study_design_scores_gemma":[0.00005609338,0.001461662,0.8543007,0.0009882092,0.00038049,0.004298705,0.05937448,0.0110903,0.0139185,0.002967005,0.05094085,0.0002231018],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752456,0.001305867,0.006373938,0.0005097789,0.00004709336,0.0002817561,0.0001421813,0.0000454469,0.01604831],"genre_scores_gemma":[0.993364,0.0004695463,0.004401602,0.00004637032,0.00001168155,0.00004825876,0.00008183821,0.00001566226,0.001560971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005896254,"threshold_uncertainty_score":0.02303594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01653442342973657,"score_gpt":0.1888200564536965,"score_spread":0.1722856330239599,"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."}}