{"id":"W4308462366","doi":"10.1016/j.applthermaleng.2022.119522","title":"An experimental study integrated with prediction using deep learning method for active/passive cooling of a modified heat sink","year":2022,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Nanofluid Flow and Heat Transfer","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Heat sink; Passive cooling; Active cooling; Sink (geography); Mechanical engineering; Materials science; Engineering; Environmental science; Water cooling; Meteorology; Thermal; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001704064,0.0002607334,0.0003258252,0.000178949,0.0001846971,0.00002068967,0.0001424745,0.00005672815,0.00003341048],"category_scores_gemma":[0.000002371755,0.000266634,0.00005619001,0.0002787946,0.00001134917,0.0001342921,0.00002376454,0.0003744549,2.524771e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002120373,"about_ca_system_score_gemma":0.00001817792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004932066,"about_ca_topic_score_gemma":0.000002128973,"domain_scores_codex":[0.9988987,0.00003206456,0.0002708256,0.0002606767,0.0002183667,0.0003194096],"domain_scores_gemma":[0.9996455,0.00006457401,0.00001835176,0.0001622859,0.00003405072,0.00007528175],"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":[0.0002279026,0.00009699269,0.00001283433,0.00002304181,0.00009500612,0.000001901455,0.001995769,0.517117,0.4795496,0.0001268526,1.575708e-7,0.0007529841],"study_design_scores_gemma":[0.001273705,0.0005165873,0.0001234897,0.00001114589,0.00005944128,0.000006649352,0.003633214,0.7291054,0.2650366,0.000001065606,0.00002970039,0.0002030157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5759686,0.00005300467,0.4230211,4.265042e-7,0.00008255115,0.0005504683,0.00001672738,0.000251289,0.00005583123],"genre_scores_gemma":[0.9875311,0.000001124386,0.01168244,0.00000378721,0.00007052062,0.0005383674,0.00004593992,0.0001245523,0.000002158138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4115625,"threshold_uncertainty_score":0.9999786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109514060793695,"score_gpt":0.2345707713358491,"score_spread":0.2236193652564796,"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."}}