{"id":"W2965725938","doi":"10.18280/rcma.290201","title":"Soft Computing Approaches for Thermal Conductivity Estimation of CNT/Water Nanofluid","year":2019,"lang":"fr","type":"article","venue":"Revue des composites et des matériaux avancés","topic":"Nanofluid Flow and Heat Transfer","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nanofluid; Thermal conductivity; Materials science; Soft computing; Conductivity; Thermal; Artificial neural network; Composite material; Nanotechnology; Thermodynamics; Computer science; Nanoparticle; Artificial intelligence; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006592873,0.0005244275,0.0008951811,0.0001804595,0.0001850161,0.0001217045,0.000307069,0.0003338697,0.0002374193],"category_scores_gemma":[0.00003160121,0.0004689504,0.0003761335,0.0001712307,0.0002969678,0.0006504866,0.00008231559,0.0003201447,0.0002621149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002019257,"about_ca_system_score_gemma":0.00004820223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000759503,"about_ca_topic_score_gemma":0.00001452035,"domain_scores_codex":[0.9975427,0.0001657883,0.0007603278,0.0005047416,0.0002030616,0.0008233461],"domain_scores_gemma":[0.998553,0.00055406,0.00007573798,0.0004774244,0.0001912837,0.0001485411],"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":[0.0001023677,0.0002008618,0.001194929,0.009621673,0.0002518745,0.000004739989,0.003173797,0.5739455,0.3648676,0.01234588,0.00006960629,0.0342212],"study_design_scores_gemma":[0.001109263,0.0002906638,0.001053132,0.00125552,0.0001886134,0.00007253967,0.00005479909,0.7229238,0.269334,0.001930939,0.001261221,0.000525577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4814888,0.004240446,0.5111963,0.0001545919,0.0008735826,0.000821621,0.00009041955,0.0001245715,0.001009684],"genre_scores_gemma":[0.8122483,0.0001798551,0.186067,0.00002757088,0.0002489269,0.00003255952,0.0001528413,0.000140171,0.0009026743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3307596,"threshold_uncertainty_score":0.9997762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07143035295894141,"score_gpt":0.2600581967862963,"score_spread":0.1886278438273549,"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."}}