{"id":"W4328050886","doi":"10.1016/j.enganabound.2023.03.009","title":"Prediction and evaluation of energy and exergy efficiencies of a nanofluid-based photovoltaic-thermal system with a needle finned serpentine channel using random forest machine learning approach","year":2023,"lang":"en","type":"article","venue":"Engineering Analysis with Boundary Elements","topic":"Solar Thermal and Photovoltaic Systems","field":"Energy","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"Narodowe Centrum Nauki","keywords":"Exergy; Nanofluid; Exergy efficiency; Reynolds number; Photovoltaic system; Thermal efficiency; Materials science; Thermal; Efficient energy use; Thermal energy; Environmental science; Process engineering; Mechanical engineering; Mechanics; Thermodynamics; Physics; Electrical engineering; Engineering; Chemistry","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":[0.0002286575,0.0003078197,0.0004526702,0.000242945,0.0004008896,0.000399662,0.0003322249,0.0005969048,0.000581633],"category_scores_gemma":[0.0003394533,0.0001649731,0.0004002975,0.0001854329,0.0003101876,0.0004428911,0.0002065,0.0002468193,0.00008729406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004698947,"about_ca_system_score_gemma":0.0004094732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00618832,"about_ca_topic_score_gemma":0.004419419,"domain_scores_codex":[0.9999291,0.00001021901,0.000004024786,0.00002138376,0.00001863333,0.00001670287],"domain_scores_gemma":[0.9997974,0.0001145196,0.0000216902,0.0000115821,0.00004234038,0.0000125399],"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":[0.0001251147,0.00007043569,0.003484904,0.00003987663,0.00001222442,0.00005367886,0.0000133123,0.9745783,0.01555455,0.0002582356,0.00006381804,0.005745522],"study_design_scores_gemma":[0.000002455464,0.00003118855,0.0006967451,8.004656e-7,0.000002934489,0.000003176103,0.000004166127,0.995662,0.003536504,0.00003974518,0.00001759792,0.000002670148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9617413,0.00009444913,0.03654141,0.00003250441,0.00001228913,0.00001616181,0.00006665421,0.0001503579,0.001344881],"genre_scores_gemma":[0.9979091,0.00001416894,0.001815204,0.000001683062,5.702123e-7,0.000005843352,0.00001915393,0.000002290573,0.0002318976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00618832,"threshold_uncertainty_score":0.0123046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617327586230155,"score_gpt":0.2050650792033841,"score_spread":0.1888918033410826,"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."}}