{"id":"W2755953131","doi":"10.1111/mice.12306","title":"Automated Model‐Based Finding of 3D Objects in Cluttered Construction Point Cloud Models","year":2017,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Computer science; Process (computing); Computer vision; Artificial intelligence; Identification (biology); Matching (statistics); Point (geometry); Object (grammar); Key (lock); Isolation (microbiology); Feature (linguistics); Measure (data warehouse); Task (project management); Data mining; 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":[0.0001807794,0.000211219,0.0003051931,0.0001182178,0.0001720674,0.0001240692,0.0002303021,0.0001208693,0.00003744914],"category_scores_gemma":[0.00002846688,0.0001832771,0.00004459739,0.00009050655,0.00007045549,0.000461323,0.00003387623,0.0002153371,0.000001192489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001002754,"about_ca_system_score_gemma":0.00003247497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001880011,"about_ca_topic_score_gemma":0.0002376451,"domain_scores_codex":[0.9989712,0.00002891139,0.0002947432,0.0002700076,0.0001461973,0.0002888926],"domain_scores_gemma":[0.9994125,0.00005492074,0.0001422304,0.0002596889,0.00003354578,0.00009714373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001386222,0.000003063358,0.02269781,0.00008595925,0.00001102362,0.000007556678,0.0002935728,0.9640226,0.0003617783,0.00006378727,0.00004273478,0.01239628],"study_design_scores_gemma":[0.0004021044,0.00003873196,0.1957445,0.0001482695,0.000005138975,0.0000169437,0.00001912756,0.8028091,0.0001704025,0.0004685028,0.000005924804,0.0001712953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9290721,0.000125737,0.06930827,0.00002204977,0.0005850542,0.0001083441,0.00003837255,0.0001647702,0.0005752787],"genre_scores_gemma":[0.9665169,0.00001521506,0.03329297,0.00003097302,0.00009838269,8.020441e-7,0.00003393085,0.000006785888,0.000004042254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1730467,"threshold_uncertainty_score":0.7473825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322039835155316,"score_gpt":0.2018988000087383,"score_spread":0.1886784016571852,"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."}}