{"id":"W3119449750","doi":"10.1038/s41598-020-79986-5","title":"Innovative analytical model for temperature prediction of front-end accessory drive","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Brake Systems and Friction Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Canadian Institute for Advanced Research","funders":"Mitacs","keywords":"Dynamometer; Pulley; Thermal; Computer science; Work (physics); Heat transfer; Mechanical engineering; Ordinary differential equation; Belt drive; Range (aeronautics); Front (military); Ode; Flow (mathematics); Control theory (sociology); Mechanics; Differential equation; Engineering; Applied mathematics; Thermodynamics; Mathematics; Physics; Aerospace engineering","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":[0.000446275,0.00009432271,0.0002157193,0.0001711623,0.0001056616,0.0001019334,0.00004904129,0.00009583421,0.0001007826],"category_scores_gemma":[0.00009599084,0.00008749143,0.0001128764,0.0008698505,0.00006493984,0.0001862763,0.00001915339,0.00009494503,0.000002126237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005152444,"about_ca_system_score_gemma":0.0001019133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003180813,"about_ca_topic_score_gemma":0.00001823629,"domain_scores_codex":[0.9987294,0.00001250991,0.0004925366,0.0003430286,0.0002745792,0.000147928],"domain_scores_gemma":[0.9987058,0.00001417625,0.000106601,0.0004009013,0.0007171762,0.0000553256],"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":[0.000009615823,0.0001389924,0.00645194,0.0003738124,0.0006775737,0.0001085651,0.002435875,0.4296946,0.3278259,0.002051286,0.2272558,0.00297602],"study_design_scores_gemma":[0.0001070414,0.000005573514,0.0008819046,0.00003116756,0.0000538733,0.0000339254,0.0002559983,0.958142,0.0302381,0.001003442,0.009144672,0.0001022859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6614001,0.0008486802,0.3059846,0.00015999,0.01513624,0.0006288459,0.0002300361,0.0003888931,0.01522262],"genre_scores_gemma":[0.9906916,0.000003534355,0.0009979593,0.000007222478,0.00007045108,0.00002224715,0.0001662551,0.00001318654,0.008027537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5284474,"threshold_uncertainty_score":0.3567797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02091675840097521,"score_gpt":0.2419922148314829,"score_spread":0.2210754564305077,"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."}}