{"id":"W2963052345","doi":"","title":"Performance Evaluation of Nanocoolants for Automotive Cooling Application","year":2019,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Nanofluid Flow and Heat Transfer","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nanofluid; Thermal conductivity; Materials science; Coolant; Heat transfer; Composite material; Heat transfer coefficient; Radiator (engine cooling); Zeta potential; Viscosity; Thermodynamics; Nanoparticle; Nanotechnology; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005211191,0.00005439276,0.0001054578,0.0001250444,0.00001243295,0.00001578474,0.00007836235,0.00003899275,0.0000105802],"category_scores_gemma":[0.00002567808,0.00005069407,0.00003728321,0.00003995164,0.000007641295,0.0002172556,0.000004304271,0.00004459129,0.000002442958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007473196,"about_ca_system_score_gemma":0.00003782198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.317554e-7,"about_ca_topic_score_gemma":6.580165e-7,"domain_scores_codex":[0.9991853,0.000009351294,0.0002964096,0.0000447721,0.0004112188,0.00005291799],"domain_scores_gemma":[0.998463,0.00003928536,0.00008483628,0.00003800977,0.001355998,0.00001887658],"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":[0.0000449886,0.00002838909,0.002951096,0.0000829991,0.0001203161,1.383178e-7,0.0002848814,0.8611233,0.05256631,0.00105567,0.00007192329,0.08166998],"study_design_scores_gemma":[0.0008608742,0.00006254004,0.02068741,0.0001053,0.00004282793,0.00001143433,0.00002422603,0.9628496,0.01497763,0.0002053057,0.0001216169,0.00005124282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8750901,0.0001370574,0.1236658,0.00005635459,0.0006106551,0.0001962035,0.000007164797,0.00001330687,0.0002233605],"genre_scores_gemma":[0.9966288,0.0001340454,0.003108752,0.000009469483,0.00009039745,0.000004238587,0.000009335786,0.000008051431,0.000006906335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1215387,"threshold_uncertainty_score":0.2067244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01261155562424584,"score_gpt":0.2590264226409874,"score_spread":0.2464148670167415,"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."}}