{"id":"W4242141013","doi":"10.32920/ryerson.14663490","title":"Development of a soot concentration estimation library for industrial combustion applications using Lagrangian parcel tracking","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Government of Ontario; Compute Canada","keywords":"Soot; Estimator; Combustion; Laminar flow; Interpolation (computer graphics); Environmental science; Computer science; Process engineering; Aerospace engineering; Chemistry; Engineering; Mathematics; Statistics; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001075389,0.0001512853,0.0002021253,0.00006108385,0.0001016158,0.00008349457,0.0001004018,0.0002768,0.00006034668],"category_scores_gemma":[0.000007463383,0.0001631793,0.00006229767,0.0001319078,0.00001298656,0.0002300512,0.0000551845,0.000220349,7.889439e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006634269,"about_ca_system_score_gemma":0.0002694376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004057212,"about_ca_topic_score_gemma":0.000003774633,"domain_scores_codex":[0.9991035,0.00001151973,0.0004599703,0.0001715934,0.0001147039,0.0001387416],"domain_scores_gemma":[0.9995779,0.00003319207,0.0001150824,0.0001642048,0.00005855384,0.00005107381],"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":[0.00001414457,0.00005314657,0.0004535966,0.001043776,0.00007724738,2.075218e-7,0.001104757,0.7176602,0.02115346,0.000260949,0.00016339,0.2580151],"study_design_scores_gemma":[0.0003014451,0.000004052387,0.0002471137,0.0003961101,0.00003437148,9.859784e-7,0.0001472536,0.8972421,0.09956345,0.00005562075,0.001811964,0.0001954791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4006894,0.0001039906,0.5979838,0.00001711939,0.0001885987,0.0006209069,0.00002765137,0.0001422865,0.0002262945],"genre_scores_gemma":[0.7561504,0.0000266342,0.2423779,0.000005571434,0.0001312847,0.0001483422,0.00111446,0.00002726171,0.00001809249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3556058,"threshold_uncertainty_score":0.6654257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06364035509630835,"score_gpt":0.2737400921627826,"score_spread":0.2100997370664742,"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."}}