{"id":"W4235105819","doi":"10.32920/ryerson.14657025","title":"Prediction Of Particle Laden Flow In Gas Pipe","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Particle Dynamics in Fluid Flows","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Turbulence; Mechanics; Two-phase flow; Particle (ecology); Flow (mathematics); Eddy; Dispersion (optics); Particle size; Particle number; Natural gas; Materials science; Physics; Chemistry; Geology; Thermodynamics; Volume (thermodynamics); Optics","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":[0.0001446192,0.0001202304,0.0002094342,0.00005063478,0.000005451874,0.00002024727,0.000135314,0.0001683341,0.0001515653],"category_scores_gemma":[0.00004015277,0.0001391123,0.00005635419,0.0001297306,0.00001789856,0.00005504141,0.0001760036,0.0003138738,0.00001326703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001092887,"about_ca_system_score_gemma":0.00002447997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004971478,"about_ca_topic_score_gemma":0.0001915033,"domain_scores_codex":[0.9991499,0.00002423417,0.000331734,0.0001765562,0.0001389897,0.0001786467],"domain_scores_gemma":[0.9994859,0.00002714068,0.00001781578,0.0003948215,0.00003228079,0.00004205853],"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":[0.000002887679,0.00004161032,0.0138329,0.0001980718,0.0000402377,0.00001197654,0.0005060277,0.9449722,0.03836234,0.0002031352,0.0001449048,0.001683756],"study_design_scores_gemma":[0.0001557976,0.000005181785,0.01311127,0.00008526329,0.00001115739,0.000001487736,0.000056794,0.9601786,0.02607636,0.0002086699,0.00001684234,0.00009263882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840936,0.0002803458,0.01117216,0.00002988691,0.0006537728,0.0001320373,0.00003092394,0.0002091211,0.00339815],"genre_scores_gemma":[0.9954023,0.0001389982,0.00423351,0.000005493724,0.00004580433,0.00003677932,0.00003519606,0.0000258188,0.00007607336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01520639,"threshold_uncertainty_score":0.5672833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02172063887946942,"score_gpt":0.2202366540647011,"score_spread":0.1985160151852317,"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."}}