{"id":"W2538618992","doi":"10.1002/cjce.22724","title":"Development of CAMD based on the hybrid gene algorithm and simulated annealing algorithm and the application on solvent selection","year":2016,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Simulated annealing; Algorithm; Ranking (information retrieval); Computer science; Adaptive simulated annealing; Evolutionary algorithm; Annealing (glass); Selection (genetic algorithm); Solvent; Hybrid algorithm (constraint satisfaction); Fitness function; Genetic algorithm; Materials science; Chemistry; Artificial intelligence; Machine learning; Organic chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0003100713,0.00009202596,0.0001052098,0.00007191308,0.00007250821,0.00002569854,0.00009257816,0.00003386813,0.000005416651],"category_scores_gemma":[0.00006885586,0.00004420876,0.00002394047,0.00009614019,0.0000413768,0.00004417943,0.000003613145,0.0001491601,4.277263e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001305034,"about_ca_system_score_gemma":0.00007901204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004064916,"about_ca_topic_score_gemma":0.00001230166,"domain_scores_codex":[0.9994784,0.00001062957,0.000225459,0.00005581273,0.0001151463,0.00011458],"domain_scores_gemma":[0.9995635,0.0001342319,0.00006131451,0.00006635903,0.00008252331,0.00009212594],"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":[0.00002316523,0.000005666269,0.000005646835,0.00002153821,0.0000678732,0.000001068353,0.0004215796,0.8131387,0.06422582,0.0003828465,0.00006956291,0.1216366],"study_design_scores_gemma":[0.0002889058,0.000008580308,0.00001350431,0.00006070513,0.000008449668,0.000009903507,0.000005783658,0.7519621,0.247304,0.00002985404,0.0002613979,0.00004679081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1643521,0.0001766691,0.8341948,0.0009762993,0.00008187393,0.0001595144,0.000007012708,0.0000210293,0.00003074522],"genre_scores_gemma":[0.9935166,0.00001219314,0.006317374,0.00007143098,0.00005864828,0.000005020628,0.000001899783,0.00001410622,0.000002751067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8291644,"threshold_uncertainty_score":0.1802781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005038998862011639,"score_gpt":0.173436975909299,"score_spread":0.1683979770472874,"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."}}