{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003115218,0.0003314778,0.0004548286,0.0003999645,0.0001592736,0.0001215764,0.0002256492,0.0002573012,0.0000868862],"category_scores_gemma":[0.00006855205,0.0003468257,0.0001004509,0.000533365,0.0002738078,0.001262674,0.00007253684,0.0002395197,0.00002798231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008156767,"about_ca_system_score_gemma":0.00005092013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001225873,"about_ca_topic_score_gemma":0.000009266745,"domain_scores_codex":[0.9980739,0.00001222663,0.0006125581,0.0006717456,0.0003020958,0.0003275144],"domain_scores_gemma":[0.9987012,0.00002507867,0.0002530397,0.0003888773,0.0005276989,0.0001040872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005982571,0.0001583629,0.1396912,0.003944439,0.001061706,0.000001535464,0.0009746847,0.003850946,0.3170844,0.0352418,0.01018353,0.4872091],"study_design_scores_gemma":[0.004451339,0.0005848167,0.009549821,0.0007872152,0.0009369975,0.0007818531,0.005492758,0.7366444,0.1389142,0.005332933,0.0942789,0.002244795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857223,0.000318333,0.008241575,0.0001235795,0.001819077,0.001148753,0.0002097745,0.0003984412,0.002018142],"genre_scores_gemma":[0.9726313,0.0002055108,0.02575374,0.00002063251,0.0004363424,0.00004823026,0.0002783447,0.00006730195,0.000558569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7327935,"threshold_uncertainty_score":0.9998984,"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."}}