{"id":"W2044012834","doi":"10.3390/su7022243","title":"Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine","year":2015,"lang":"en","type":"article","venue":"Sustainability","topic":"Advanced Thermodynamic Systems and Engines","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Stirling engine; Torque; Robustness (evolution); Artificial neural network; Power (physics); Computer science; Control theory (sociology); Engineering; Control engineering; Artificial intelligence; Mechanical engineering; Physics; Control (management)","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":[0.0004804611,0.0003808111,0.0002652725,0.000211173,0.0001870988,0.0004732427,0.0005380561,0.0006150942,0.0003979296],"category_scores_gemma":[0.001365801,0.0002241042,0.0002830037,0.000257704,0.0003591527,0.0004590021,0.0003345805,0.0005808901,0.0001014857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006472665,"about_ca_system_score_gemma":0.0004429792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01037139,"about_ca_topic_score_gemma":0.007783445,"domain_scores_codex":[0.9999001,0.00003816749,0.00000592787,0.0000223611,0.00002169665,0.00001174116],"domain_scores_gemma":[0.9996728,0.0002167614,0.00003247393,0.00001578692,0.00005534622,0.000006837727],"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":[0.00001536232,0.000009182276,0.000289208,0.00001458929,0.000008134084,0.000009728912,0.00001146068,0.9907927,0.0006986274,0.0007219393,0.0000411799,0.007387971],"study_design_scores_gemma":[4.392034e-7,0.000002736438,0.00002419237,5.172446e-7,7.088178e-7,5.835883e-7,6.603601e-7,0.9996746,0.0001403823,0.0001357088,0.00001869246,7.419284e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2350486,0.0005248351,0.758386,0.0002941525,0.0000602445,0.00005557408,0.0001006461,0.0003731473,0.00515681],"genre_scores_gemma":[0.9655409,0.0001605291,0.03233095,0.00003259765,0.00001215562,0.00005942751,0.00005963901,0.00001186872,0.001791874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01037139,"threshold_uncertainty_score":0.02062201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615257371208439,"score_gpt":0.2566076873642473,"score_spread":0.2404551136521629,"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."}}