{"id":"W4307260628","doi":"10.4271/04-16-01-0003","title":"Closed-Loop Predictive Control of a Multi-mode Engine Including Homogeneous Charge Compression Ignition, Partially Premixed Charge Compression Ignition, and Reactivity Controlled Compression Ignition Modes","year":2022,"lang":"en","type":"article","venue":"SAE international journal of fuels and lubricants","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Ignition system; Homogeneous charge compression ignition; Compression (physics); Materials science; Charge (physics); Carbureted compression ignition model engine; Compression ratio; Mechanics; Combustion; Automotive engineering; Internal combustion engine; Thermodynamics; Physics; Chemistry; Composite material; Engineering; Combustion chamber","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003459891,0.0006127449,0.0004056008,0.0002229142,0.0004544838,0.0006871694,0.0007603141,0.0003962138,0.00156465],"category_scores_gemma":[0.0004396499,0.0002465359,0.0003551213,0.0001469345,0.0003539277,0.0003564075,0.0003894015,0.0006253226,0.0003218389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005883838,"about_ca_system_score_gemma":0.0008426845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0124101,"about_ca_topic_score_gemma":0.01121811,"domain_scores_codex":[0.9998695,0.00001741258,0.000005143399,0.00004584992,0.00004528616,0.00001686559],"domain_scores_gemma":[0.9998531,0.00004471286,0.00002196653,0.00001613113,0.00005544138,0.000008656303],"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":[0.0003743171,0.00021807,0.001688431,0.0001731922,0.00004696333,0.0001646826,0.0001510286,0.8674352,0.0576505,0.003165874,0.00173654,0.06719521],"study_design_scores_gemma":[0.00001748239,0.00009725973,0.00039726,0.000004234451,0.000009569384,0.00001094138,0.000005940599,0.991838,0.00679408,0.0001960531,0.0006217294,0.000007412878],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2995642,0.0003320854,0.6712793,0.000317385,0.0002467571,0.0002447247,0.0003218298,0.003742392,0.02395128],"genre_scores_gemma":[0.9825928,0.00005927417,0.01437965,0.00002129038,0.000009302658,0.00007757382,0.0000900667,0.00002277599,0.002747306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0124101,"threshold_uncertainty_score":0.02467573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02231916967369766,"score_gpt":0.2816395428836184,"score_spread":0.2593203732099207,"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."}}