{"id":"W2081583556","doi":"10.1002/cjce.21731","title":"Application of genetic algorithms in design and optimisation of multi‐stream plate–fin heat exchangers","year":2012,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Heat Transfer and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pressure drop; Heat exchanger; Fin; Genetic algorithm; Selection (genetic algorithm); Current (fluid); Mathematical optimization; Computer science; Optimal design; Mechanical engineering; Mathematics; Engineering; Mechanics; Artificial intelligence; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001902659,0.00007074059,0.0001320154,0.0001260959,0.000007822284,0.000004711841,0.0000692332,0.00006158462,0.000003618237],"category_scores_gemma":[0.00002099682,0.00006339464,0.00002293979,0.0001259459,0.00002205303,0.00007585163,0.000001909465,0.0001160776,1.619577e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000841974,"about_ca_system_score_gemma":0.00003397493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000378166,"about_ca_topic_score_gemma":0.00003358225,"domain_scores_codex":[0.9994895,0.0000087175,0.0002535144,0.00003198683,0.00006707353,0.0001492281],"domain_scores_gemma":[0.9996955,0.00004672257,0.00001654097,0.00005741505,0.00003006898,0.0001537278],"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":[0.00000253634,0.000003922143,0.0004135422,0.00004625276,0.00001171027,5.641233e-7,0.0008636983,0.9031835,0.09278957,0.00001021937,0.000003030601,0.002671415],"study_design_scores_gemma":[0.0002497363,0.00001024494,0.001435826,0.0000490455,0.00001529822,0.00001999078,0.00001488276,0.7994418,0.1986809,0.000005934976,0.00001355065,0.00006281838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5063323,0.001217409,0.4922541,0.00002596967,0.00005652288,0.00009728892,0.000002662645,0.000005805412,0.000007928971],"genre_scores_gemma":[0.974777,0.00003257844,0.02512815,0.000003034273,0.00004049037,0.000003045211,0.000001348508,0.00001405465,3.29685e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4684447,"threshold_uncertainty_score":0.2585158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01267024934677832,"score_gpt":0.1916807724895565,"score_spread":0.1790105231427782,"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."}}