{"id":"W2130208639","doi":"10.1002/cjce.22009","title":"Application of a capacitance sensor for monitoring water lubricated pipeline flows","year":2014,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Multiphysics; Capacitance; Annulus (botany); Capacitance probe; Pipeline transport; Pipeline (software); Flow (mathematics); Mechanical engineering; Materials science; Viscosity; Layer (electronics); Mechanics; Acoustics; Petroleum engineering; Engineering; Composite material; Structural engineering; Chemistry; Physics; Finite element method; Electrode","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003083439,0.00009856691,0.000194473,0.0001055055,0.00002641808,0.00001555588,0.000184153,0.00004948048,0.000004064954],"category_scores_gemma":[0.00007336042,0.00006954718,0.0001035004,0.0001170106,0.00001538499,0.00004994238,0.000002099993,0.0001496484,0.000001683214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000100344,"about_ca_system_score_gemma":0.00001896282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001169639,"about_ca_topic_score_gemma":0.00003563016,"domain_scores_codex":[0.999306,0.000005008679,0.0002992142,0.00005208984,0.0001179509,0.0002196971],"domain_scores_gemma":[0.9994957,0.00004561765,0.00003996949,0.0001221831,0.0001381752,0.0001583956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002573896,0.000001350898,0.0000766791,0.00004696527,0.00004705075,3.413708e-7,0.0001047622,0.2091724,0.7894776,0.00002225851,0.00005486458,0.0009931272],"study_design_scores_gemma":[0.0001352581,0.0000051902,0.00001456131,0.00003796998,0.00004619439,0.000004970415,0.000004105052,0.4188432,0.579522,0.00003144035,0.001287623,0.00006750296],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.847298,0.0003489993,0.1517097,0.0002108486,0.0002578204,0.00009106237,0.000004492228,0.00002711444,0.0000519726],"genre_scores_gemma":[0.9974836,0.000001853889,0.002067899,0.000005314077,0.0004030908,0.00000531576,0.000002218177,0.00002342652,0.000007244111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2099556,"threshold_uncertainty_score":0.2836052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008949731891728582,"score_gpt":0.1751388221836001,"score_spread":0.1661890902918715,"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."}}