{"id":"W2955276466","doi":"10.29173/mocs73","title":"Data Analytics of Production Cycle Time for Offsite Construction Projects","year":2019,"lang":"en","type":"article","venue":"Modular and Offsite Construction (MOC) Summit Proceedings","topic":"BIM and Construction Integration","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Schedule; Productivity; Context (archaeology); Production (economics); Work (physics); Duration (music); Process (computing); Factory (object-oriented programming); Product (mathematics); Analytics; Computer science; Operations research; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0007540297,0.0005972972,0.0003341651,0.004915674,0.0002825144,0.0007652937,0.0005222659,0.0003355735,0.002259218],"category_scores_gemma":[0.003281194,0.0002072889,0.0004257015,0.005415885,0.000163797,0.0004089555,0.0003109492,0.0004423346,0.0009808658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001046759,"about_ca_system_score_gemma":0.001266101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03687343,"about_ca_topic_score_gemma":0.05108342,"domain_scores_codex":[0.9992282,0.00005660038,0.00006535653,0.0001807394,0.0004018036,0.00006725385],"domain_scores_gemma":[0.9973207,0.0008221925,0.0004816168,0.0002793333,0.0009574838,0.0001386455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009614142,0.0006788616,0.5308614,0.0009144708,0.0001433763,0.0007144382,0.001852111,0.08799466,0.01905005,0.00258129,0.01391778,0.3403301],"study_design_scores_gemma":[0.00003418495,0.0003896781,0.7103535,0.0001193665,0.00006913523,0.0003566736,0.001698095,0.2391202,0.01742883,0.001401648,0.02893664,0.0000919497],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8751442,0.0003551124,0.03228048,0.0002139084,0.00005190652,0.0002816443,0.07961617,0.002836169,0.009220345],"genre_scores_gemma":[0.9159566,0.0002737578,0.03014045,0.00002002485,0.00001583108,0.0002610539,0.05005686,0.0001342995,0.00314121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03687343,"threshold_uncertainty_score":0.07331759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515735602452109,"score_gpt":0.2105321392015118,"score_spread":0.1953747831769907,"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."}}