{"id":"W2768891853","doi":"10.1080/19942060.2017.1400471","title":"Development and testing of a soot particle concentration estimator using Lagrangian post-processing","year":2017,"lang":"en","type":"article","venue":"Engineering Applications of Computational Fluid Mechanics","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Soot; Estimator; Laminar flow; Combustion; Interpolation (computer graphics); Diffusion flame; Process engineering; Computer science; Environmental science; Aerospace engineering; Combustor; Engineering; Mechanical engineering; Chemistry; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001706232,0.0004073765,0.0004489443,0.0004788344,0.000351136,0.0005705941,0.0009832728,0.0006279791,0.001016542],"category_scores_gemma":[0.003461413,0.0002901335,0.0004443931,0.0002908624,0.0002704377,0.0007329325,0.0004717755,0.0004136999,0.0003757025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004412285,"about_ca_system_score_gemma":0.001252943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002943956,"about_ca_topic_score_gemma":0.002323776,"domain_scores_codex":[0.9995414,0.00007805762,0.00003138634,0.0000952361,0.0002190465,0.00003498662],"domain_scores_gemma":[0.9984256,0.0006919958,0.0001358316,0.0001994998,0.0005036321,0.00004358587],"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":[0.0005600483,0.0004955894,0.01949795,0.0003310298,0.0001306308,0.0001860896,0.0001424248,0.6026341,0.1218173,0.005628393,0.001078962,0.2474974],"study_design_scores_gemma":[0.00002031447,0.000123092,0.001230608,0.000005544527,0.000008845009,0.00001881148,0.00001207065,0.9492574,0.04848749,0.0002323083,0.0005927228,0.00001076427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1791413,0.00007533166,0.8169545,0.0000749924,0.00004286942,0.0001786764,0.000221359,0.002226906,0.001084129],"genre_scores_gemma":[0.4693891,0.0000826612,0.5286455,0.0000361884,0.00001074203,0.0001738492,0.0004793499,0.0001721771,0.001010582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002943956,"threshold_uncertainty_score":0.009023488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02179452010923597,"score_gpt":0.262784965491342,"score_spread":0.240990445382106,"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."}}