{"id":"W2004264443","doi":"10.1016/j.applthermaleng.2009.04.017","title":"Diesel oxidation catalyst and particulate filter modeling in active – Flow configurations","year":2009,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Catalytic Processes in Materials Science","field":"Materials Science","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Networks of Centres of Excellence of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ford Motor Company","keywords":"Diesel particulate filter; Diesel fuel; Automotive engineering; Flow (mathematics); Particulates; Transient (computer programming); Efficient energy use; Diesel engine; Energy flow; Active cooling; Energy (signal processing); Engineering; Materials science; Environmental science; Computer science; Mechanics; Mechanical engineering; Water cooling; Chemistry; Electrical engineering","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":[0.0003738865,0.0006614443,0.0007977867,0.0004110197,0.0006956785,0.001195566,0.001191909,0.001887042,0.002129883],"category_scores_gemma":[0.0009572839,0.0005147662,0.0007979136,0.0003769962,0.0004735629,0.001284095,0.0004956177,0.0006273064,0.0003428072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123982,"about_ca_system_score_gemma":0.0009922751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03083054,"about_ca_topic_score_gemma":0.01216346,"domain_scores_codex":[0.9998515,0.00003653042,0.000006217576,0.00003207092,0.00003463087,0.00003902895],"domain_scores_gemma":[0.9996309,0.0002111815,0.00002829879,0.00002578492,0.00006822177,0.00003571009],"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":[0.00007262224,0.00005136616,0.0006978985,0.00001660759,0.000009243578,0.00005246901,0.00001601456,0.9939737,0.001919788,0.001658587,0.0001478771,0.001383852],"study_design_scores_gemma":[0.00001176425,0.00001216567,0.00013795,0.000001122521,0.000003384696,0.000003837309,0.000006600592,0.9986641,0.000833717,0.0002239466,0.00009848506,0.000002770513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9076346,0.0003397819,0.06697646,0.0004261976,0.00007965404,0.0000729876,0.0003383607,0.0003315938,0.02380029],"genre_scores_gemma":[0.9943071,0.00006532078,0.002122097,0.00002778349,0.00001439035,0.00002434587,0.0001011559,0.00003898402,0.003298884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03083054,"threshold_uncertainty_score":0.06130219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01148501100301854,"score_gpt":0.2109136111415902,"score_spread":0.1994286001385717,"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."}}