{"id":"W2587408433","doi":"10.4271/2017-01-0678","title":"Improvement on Energy Efficiency of the Spark Ignition System","year":2017,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"University of Windsor; Ford Motor Company","keywords":"SPARK (programming language); Ignition system; Energy (signal processing); Automotive engineering; Computer science; Efficient energy use; Nuclear engineering; Electrical engineering; Aerospace engineering; Engineering; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003661498,0.0007352739,0.000843718,0.0001778941,0.0006929508,0.00007713139,0.002696599,0.0007825732,0.00008831902],"category_scores_gemma":[0.001750211,0.0005372241,0.0005201505,0.000428059,0.001256201,0.0003289096,0.001006278,0.001239755,0.00004356696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005668314,"about_ca_system_score_gemma":0.00005279674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004306882,"about_ca_topic_score_gemma":0.002503877,"domain_scores_codex":[0.9959862,0.00005650954,0.001057957,0.001003715,0.001051717,0.000843865],"domain_scores_gemma":[0.9951767,0.0003464764,0.0005940835,0.003554473,0.0001421175,0.0001861093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001138562,0.0002211072,0.00001894001,0.00008906051,0.0000305797,0.00001197612,0.000005101569,0.0009707522,0.8404548,0.1524017,0.0003506448,0.00533154],"study_design_scores_gemma":[0.002591519,0.003195109,0.842274,0.002469433,0.0002365903,0.0001047863,0.0003837248,0.0000188349,0.1125397,0.008690772,0.02540637,0.002089174],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7192671,0.0004425498,0.0003797324,0.009649672,0.001979586,0.002798453,0.0002536415,0.01611251,0.2491167],"genre_scores_gemma":[0.997023,0.0000921641,0.001450686,0.0003390449,0.0001198272,0.0003627785,0.00001251321,0.0001182375,0.0004817224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.842255,"threshold_uncertainty_score":0.9997079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065047019074486,"score_gpt":0.2307827635560353,"score_spread":0.2201322933652904,"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."}}