{"id":"W4390024572","doi":"10.18280/mmep.100629","title":"Optimizing Expenditure Functions Through Economic Cybernetics: Analyzing Linear and Non-Linear Programming Approaches","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cybernetics; Linear programming; Computer science; Mathematical economics; Mathematical optimization; Management science; Operations research; Economics; Mathematics; Artificial intelligence; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004996685,0.0002540513,0.0004909947,0.0001607393,0.0001622861,0.0001372492,0.0001142288,0.0001475621,0.0000221957],"category_scores_gemma":[0.000018948,0.0002769225,0.00008760981,0.0001054589,0.00005736255,0.0002513563,0.0001100252,0.0002334632,0.0001518022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004269727,"about_ca_system_score_gemma":0.000006854363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003944873,"about_ca_topic_score_gemma":0.000001217995,"domain_scores_codex":[0.998424,0.000004299012,0.0005956658,0.0005044689,0.00002509836,0.0004464867],"domain_scores_gemma":[0.9994006,0.0001021026,0.0001193546,0.0002461549,0.000007439591,0.0001243244],"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":[0.00000243262,0.00001512338,0.00006617377,0.0002702032,0.00005715273,9.260628e-7,0.002276999,0.8466746,0.000004526085,0.1503978,0.00001334483,0.0002207075],"study_design_scores_gemma":[0.0002384399,0.00003016981,0.000006009584,0.00009677275,0.00001565297,0.000007864525,0.0002421512,0.9442429,0.00001487088,0.0522308,0.002565808,0.0003085714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1399379,0.001095979,0.8568528,0.0001650508,0.000159583,0.0002557747,0.00002653043,0.0002269173,0.001279472],"genre_scores_gemma":[0.8711338,0.0006006462,0.1272929,0.00001068497,0.0001939948,0.000100811,0.00002328793,0.00007981856,0.0005640478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7311959,"threshold_uncertainty_score":0.9999683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06542242330256204,"score_gpt":0.2162124538905566,"score_spread":0.1507900305879946,"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."}}