{"id":"W2897628261","doi":"10.1016/j.aei.2018.10.003","title":"Random generation of industrial pipelines’ data using Markov chain model","year":2018,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"BIM and Construction Integration","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pipeline transport; Markov chain; Pipeline (software); Computer science; Process (computing); Markov process; Data mining; Markov model; Engineering; Statistics; Machine learning; Mathematics; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001406696,0.0001389558,0.0001665996,0.0001235497,0.0000363558,0.00002120246,0.0001780722,0.0001042991,0.00001041065],"category_scores_gemma":[0.00005688945,0.0001431773,0.00002508674,0.0001981293,0.00002761919,0.0007374542,0.0000384117,0.0001352971,0.000004299957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004484013,"about_ca_system_score_gemma":0.0000284493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001843115,"about_ca_topic_score_gemma":0.000002619579,"domain_scores_codex":[0.9991283,0.000003632886,0.0005097358,0.00006407986,0.0001390904,0.0001551034],"domain_scores_gemma":[0.9994097,0.00001703376,0.00007187443,0.00036584,0.00009386164,0.00004164516],"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":[0.000008314688,0.000002303241,0.000008931856,0.00003559249,0.00001682722,5.667295e-8,0.0002014193,0.9569757,0.0122808,0.000355654,0.0002019673,0.02991245],"study_design_scores_gemma":[0.0007314126,0.00001224138,0.00000150481,0.00004764459,0.00001629358,0.000006519938,0.00004779548,0.9844834,0.0130829,0.00001759293,0.00140829,0.0001444607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1315314,0.00004348027,0.8668131,0.000002952501,0.0008944271,0.0001160976,0.00004028279,0.0001656618,0.0003925498],"genre_scores_gemma":[0.7454551,0.00003614633,0.2538878,0.00001224938,0.0004444756,0.000005886956,0.0001191536,0.00002582125,0.00001335908],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6139237,"threshold_uncertainty_score":0.5838601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04890236337706719,"score_gpt":0.2463009613770248,"score_spread":0.1973985979999576,"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."}}