{"id":"W4385657805","doi":"10.1016/j.autcon.2023.105045","title":"Quality assurance for building components through point cloud segmentation leveraging synthetic data","year":2023,"lang":"en","type":"article","venue":"Automation in Construction","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Point cloud; Segmentation; Quality assurance; Workload; Computer science; Schedule; Point (geometry); Quality (philosophy); Field (mathematics); Data mining; Cloud computing; Artificial intelligence; Engineering; Operations management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001839733,0.0009608008,0.0006519038,0.002025629,0.0004707748,0.00231514,0.001024963,0.0007862872,0.001582479],"category_scores_gemma":[0.006659518,0.0005353913,0.0007476022,0.001423911,0.0007626833,0.001391548,0.001414478,0.000791627,0.0008090852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000795072,"about_ca_system_score_gemma":0.001380443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00501105,"about_ca_topic_score_gemma":0.006343462,"domain_scores_codex":[0.9976103,0.0002518475,0.0001227421,0.0003287469,0.001529651,0.0001567956],"domain_scores_gemma":[0.9948673,0.0009521645,0.0006200498,0.001286112,0.002170371,0.000104048],"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":[0.0007388623,0.0001801727,0.02605306,0.0004451312,0.0001401873,0.0002288588,0.0005410966,0.3269466,0.1411126,0.00553979,0.00438901,0.4936846],"study_design_scores_gemma":[0.00001373474,0.00008842394,0.007586978,0.00002893444,0.00003371345,0.0001375799,0.00009675833,0.9390186,0.04803018,0.00227903,0.002663574,0.00002252312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09724612,0.0002904803,0.8960502,0.000140766,0.00005460936,0.00007217454,0.00043124,0.004409268,0.001305129],"genre_scores_gemma":[0.7690693,0.0001802166,0.227744,0.00005452352,0.00001885052,0.00004208697,0.001641766,0.0005646048,0.0006846068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00501105,"threshold_uncertainty_score":0.009963751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1133896092059473,"score_gpt":0.3304664351853311,"score_spread":0.2170768259793838,"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."}}