{"id":"W2936733265","doi":"10.29173/mocs7","title":"Optimum Assembly Planning for Modular Construction Using BIM and 3D Point Clouds","year":2016,"lang":"en","type":"article","venue":"Modular and Offsite Construction (MOC) Summit Proceedings","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Rework; Modular design; Point cloud; Component (thermodynamics); Computer science; Matching (statistics); Point (geometry); Engineering drawing; Systems engineering; Industrial engineering; Reliability engineering; Engineering; Embedded system; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0004555077,0.0003025977,0.0003413491,0.0001472082,0.0006332946,0.0002598704,0.0001040218,0.0002021197,0.00009080346],"category_scores_gemma":[0.00007522412,0.0002232467,0.00008167877,0.0002045329,0.0003702369,0.0009836578,0.0000260196,0.000140382,0.000007189214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001883072,"about_ca_system_score_gemma":0.00003119783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001258307,"about_ca_topic_score_gemma":0.00002527858,"domain_scores_codex":[0.9983031,0.00002798476,0.0003602763,0.0006120207,0.0002393076,0.0004572715],"domain_scores_gemma":[0.9992015,0.00006983939,0.0001904786,0.00009816376,0.0002121005,0.0002278571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002034127,0.00001029859,0.7616491,0.0001564162,0.00008009997,0.000005987191,0.0002888342,0.0001718368,0.02020508,0.001014588,0.0001069021,0.2161075],"study_design_scores_gemma":[0.01025854,0.001751116,0.6070656,0.001994762,0.0006663605,0.003399393,0.01441608,0.2945206,0.02044064,0.01453633,0.02661004,0.004340489],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853081,0.0008229385,0.01210603,0.0001801586,0.000595156,0.0003298856,0.00009337786,0.0001304327,0.0004339785],"genre_scores_gemma":[0.961522,0.0001447476,0.03782487,0.00006751194,0.0002734775,0.000004550684,0.00003036312,0.00001384028,0.0001186155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2943488,"threshold_uncertainty_score":0.9103735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02397116117056922,"score_gpt":0.2285855493674807,"score_spread":0.2046143881969115,"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."}}