{"id":"W4200113417","doi":"10.1155/2021/4892855","title":"A Deep Transfer NOx Emission Inversion Model of Diesel Vehicles with Multisource External Influence","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Automotive engineering; Autoencoder; Diesel engine; Engineering; Feature selection; Diesel fuel; Test data; Inversion (geology); Computer science; Simulation; Artificial intelligence; Deep learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002827861,0.000604652,0.0004306515,0.0002381547,0.0002215824,0.0004582083,0.0009960163,0.0006555695,0.001113287],"category_scores_gemma":[0.0004536076,0.0003540267,0.0006961199,0.0002519077,0.000359026,0.000644533,0.0005951697,0.0008876823,0.0001998831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005802748,"about_ca_system_score_gemma":0.0008941572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02261803,"about_ca_topic_score_gemma":0.0133096,"domain_scores_codex":[0.9999096,0.00001150584,0.000003933395,0.00003480522,0.000021522,0.00001855834],"domain_scores_gemma":[0.9999168,0.00002465243,0.00001191735,0.000005079608,0.00003429408,0.000007181707],"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":[0.000038138,0.00002530544,0.001479939,0.00002857971,0.00002115626,0.00007800366,0.00002635039,0.9810014,0.001733613,0.001583871,0.0004478223,0.01353581],"study_design_scores_gemma":[0.000001480514,0.000003246491,0.00009031446,8.943173e-7,0.000001887908,0.000003220988,0.000001713671,0.999469,0.0001239842,0.0002338931,0.00006875573,0.000001678463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1920598,0.0006408484,0.7985626,0.0006152402,0.000113561,0.00004662938,0.0004022098,0.0007618036,0.006797326],"genre_scores_gemma":[0.9780165,0.0002386838,0.0146404,0.0001225935,0.00002425185,0.00006960405,0.0004158911,0.00003560558,0.006436519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02261803,"threshold_uncertainty_score":0.04497278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00687970212374943,"score_gpt":0.2096750258819806,"score_spread":0.2027953237582311,"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."}}