{"id":"W4307742220","doi":"10.1016/j.clet.2022.100581","title":"Using dual mutation particle swarm method to optimize the variable cross-section of a thermoelectric generator based on a comprehensive thermodynamic model","year":2022,"lang":"en","type":"article","venue":"Cleaner Engineering and Technology","topic":"Advanced Thermoelectric Materials and Devices","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thermoelectric generator; Particle swarm optimization; Computer science; Power (physics); Generator (circuit theory); Dual (grammatical number); Variable (mathematics); Cross section (physics); Mathematical optimization; Thermoelectric effect; Applied mathematics; Mathematics; Algorithm; Thermodynamics; Physics; Mathematical analysis","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":[0.0002800759,0.0001258414,0.0001874874,0.0001526813,0.0002115535,0.00002870666,0.0001375378,0.00005908648,0.00003916574],"category_scores_gemma":[0.00003276239,0.0001005459,0.00002012604,0.0006084255,0.00003451373,0.00004214282,0.0000831254,0.0001309703,0.000001031345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006209256,"about_ca_system_score_gemma":0.00003587411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002848048,"about_ca_topic_score_gemma":3.813772e-7,"domain_scores_codex":[0.9991173,0.0000624871,0.0001980329,0.0002547614,0.0001332989,0.0002341552],"domain_scores_gemma":[0.9995093,0.00009856161,0.00008828833,0.0002148322,0.00006017081,0.00002878871],"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":[0.00004414215,0.00001485675,0.000002078096,0.00000813202,0.000003655351,0.000001580219,0.00003243925,0.5028702,0.4948276,0.001753895,8.248537e-7,0.0004405886],"study_design_scores_gemma":[0.0002138574,0.0001782184,0.00001747103,0.000004149609,0.00001039514,0.00002649347,0.0000310778,0.7008333,0.2981066,0.0004586706,0.00003730475,0.00008246542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.662998,0.00007354082,0.3365075,0.00004967929,0.00009488023,0.0001448879,0.00001181812,0.0001070596,0.00001261851],"genre_scores_gemma":[0.9531707,0.000001665863,0.04658889,0.00009031289,0.00002520355,0.00008508698,0.000001972507,0.00002343395,0.00001281059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2901726,"threshold_uncertainty_score":0.4100142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162837101825643,"score_gpt":0.2709081679993514,"score_spread":0.2546244578167871,"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."}}