{"id":"W3147942149","doi":"10.1016/j.autcon.2021.103686","title":"Quantitative investigation on the accuracy and precision of Scan-to-BIM under different modelling scenarios","year":2021,"lang":"en","type":"article","venue":"Automation in Construction","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Building information modeling; Computer science; Automation; Ground truth; Data mining; Information model; Systems engineering; Artificial intelligence; Engineering; Software engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002340029,0.00006773289,0.00008900282,0.00005764811,0.0001058616,0.00004449976,0.0000390155,0.00003939386,0.0001229479],"category_scores_gemma":[0.0001726534,0.00004542934,0.00001638296,0.000200975,0.00005839499,0.0001814095,0.000005580388,0.00007920933,0.00001301847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008548001,"about_ca_system_score_gemma":0.00002777572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003086077,"about_ca_topic_score_gemma":0.0009796371,"domain_scores_codex":[0.9992036,0.0002062376,0.0002002673,0.0001480801,0.0001652865,0.00007652759],"domain_scores_gemma":[0.9991759,0.0005506204,0.00008605199,0.00008570366,0.00006927302,0.00003243102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0000806609,0.00002028077,0.3707606,0.00004717514,0.00001958506,0.000001424624,0.003549071,0.4794851,0.002869093,0.01949034,0.00004774583,0.123629],"study_design_scores_gemma":[0.0001266919,0.00005697994,0.5491369,0.0001316823,0.000004069449,0.000007222529,0.002054159,0.4363749,0.002660321,0.009366593,0.000009032215,0.0000714241],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935772,0.00005886891,0.005078513,0.0006760715,0.0001783818,0.0001347415,0.00001098045,0.00001972979,0.0002655307],"genre_scores_gemma":[0.9965643,0.0000315542,0.003254749,0.00008632579,0.00001109319,0.000001817321,0.00003468711,0.000001457665,0.00001399806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1783763,"threshold_uncertainty_score":0.1852555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07003548266244033,"score_gpt":0.2585687474495416,"score_spread":0.1885332647871012,"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."}}