{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001139806,0.001621585,0.00124971,0.00171964,0.0008220255,0.001426746,0.001160558,0.001041643,0.002559599],"category_scores_gemma":[0.002207918,0.001500014,0.001521627,0.001813215,0.001025546,0.001387864,0.001574551,0.000989378,0.0005946551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001476596,"about_ca_system_score_gemma":0.002231306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01267382,"about_ca_topic_score_gemma":0.02135666,"domain_scores_codex":[0.9992614,0.0001655444,0.00003481335,0.0001512593,0.0002924649,0.00009457044],"domain_scores_gemma":[0.9993306,0.0003095966,0.000107815,0.00009611053,0.0001128429,0.00004302274],"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":[0.00003203327,0.00002373825,0.0005984578,0.00003696428,0.00001407877,0.00003937549,0.00005692073,0.9661909,0.001960604,0.002811247,0.0003956283,0.02783995],"study_design_scores_gemma":[0.000003382838,0.00001886464,0.0002287411,0.000005217491,0.000005698391,0.00001285964,0.00002510669,0.9959878,0.0009761816,0.002257023,0.0004711648,0.000007792745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01715212,0.00007033554,0.9808834,0.00003245294,0.000009679085,0.00006495068,0.00008259164,0.0003924974,0.001312094],"genre_scores_gemma":[0.3538042,0.0001400194,0.6440365,0.00002261009,0.000009507294,0.0002405785,0.0004094064,0.0002742107,0.001062928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01267382,"threshold_uncertainty_score":0.02520013,"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."}}