{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008932976,0.0001035601,0.0001234509,0.0001020319,0.00002511757,0.00001780261,0.00003011051,0.00007072256,0.00004122158],"category_scores_gemma":[0.00002830161,0.0001110631,0.00004310878,0.0002997001,0.000003685491,0.000134567,0.000004083726,0.00009386978,0.000004049282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009679555,"about_ca_system_score_gemma":0.00001837922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002782334,"about_ca_topic_score_gemma":0.000001326489,"domain_scores_codex":[0.9994951,0.00001383228,0.0001886029,0.00009074964,0.00009521943,0.0001164684],"domain_scores_gemma":[0.9997512,0.0000482026,0.00002568256,0.00007247148,0.00007384518,0.00002859225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001505047,0.000004606464,0.001399479,0.0002856491,0.00004818748,0.000001979697,0.0002729254,0.532547,0.4605386,0.00228901,0.0001024225,0.002508637],"study_design_scores_gemma":[0.0000782023,0.0000134735,0.003281933,0.00005404851,0.00001397858,0.00002566679,0.0002549831,0.2959366,0.7001348,0.000003935731,0.00009371033,0.0001087435],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.328319,0.00003679188,0.670155,9.832205e-7,0.0005533921,0.00005365352,0.000001372686,0.0004288536,0.0004509681],"genre_scores_gemma":[0.9950436,0.000003817884,0.004817532,8.179537e-7,0.00006594187,0.000006551433,0.00001098339,0.00002370822,0.00002707325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6667246,"threshold_uncertainty_score":0.4529023,"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."}}