{"id":"W4281958843","doi":"10.1002/cjce.24490","title":"Shape optimization of pipeline components","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Erosion and Abrasive Machining","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Flow (mathematics); Erosion; Sensitivity (control systems); Mathematical optimization; Computer science; Particle (ecology); Work (physics); Mechanics; Mathematics; Engineering; Mechanical engineering; Physics; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004017303,0.0006867243,0.0005064235,0.0007475303,0.0002413138,0.0008227399,0.0004305312,0.0005415218,0.002165921],"category_scores_gemma":[0.0007480714,0.0003842833,0.0005068388,0.000513331,0.0004946544,0.0005227211,0.0006325537,0.0003885579,0.0004682216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006289698,"about_ca_system_score_gemma":0.0005090478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008615457,"about_ca_topic_score_gemma":0.00108341,"domain_scores_codex":[0.9997831,0.00003026449,0.000008911281,0.00003705344,0.00009859775,0.00004208558],"domain_scores_gemma":[0.9997515,0.00005667367,0.00004431004,0.000040589,0.00008525334,0.00002168308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009456844,0.00005462085,0.001047262,0.0001221949,0.0000202411,0.00008146853,0.00004891411,0.8955919,0.04247895,0.005871303,0.0005770693,0.05401153],"study_design_scores_gemma":[0.0000105206,0.0001974925,0.001161845,0.00001532155,0.00001622042,0.00007028305,0.00003356759,0.9702755,0.02085187,0.003192332,0.004155273,0.00001972208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4213601,0.00100943,0.5550148,0.0002186033,0.00007765904,0.00008233717,0.0001697984,0.0006787758,0.02138846],"genre_scores_gemma":[0.9668794,0.0001916399,0.02951242,0.00001761503,0.000006165627,0.00002560336,0.00009277181,0.0001237397,0.003150832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002165921,"threshold_uncertainty_score":0.007245719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009548704941067115,"score_gpt":0.1826167154601888,"score_spread":0.1730680105191217,"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."}}