{"id":"W1502394320","doi":"10.3968/j.pam.1925252820120302.1255","title":"On the Power Efficiency of Artificial Neural Network (ANN) and the Classical Regression Model","year":2012,"lang":"en","type":"article","venue":"Progress in applied mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Computer science; Regression; Regression analysis; Variance (accounting); Artificial intelligence; Field (mathematics); Key (lock); Machine learning; Statistics; Mathematics","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":[0.0007519387,0.0001355979,0.000198559,0.000023065,0.0001715718,0.00006582266,0.0005984895,0.00005415588,0.000002646025],"category_scores_gemma":[0.00002029801,0.00006262417,0.00004248788,0.0003235107,0.000374058,0.00005559998,0.0002909034,0.0002365247,0.000005077392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008149844,"about_ca_system_score_gemma":0.00001011198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.230617e-7,"about_ca_topic_score_gemma":3.42553e-7,"domain_scores_codex":[0.9988735,0.00003671112,0.0003188764,0.0001729763,0.0002775561,0.0003203485],"domain_scores_gemma":[0.9985534,0.000658576,0.0001814297,0.0005395274,0.00001723011,0.00004982937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001445889,0.0001881087,0.00002425416,0.0000110345,0.00000276918,1.495722e-7,0.0008757737,0.002262082,0.00001932686,0.9854015,0.0004566144,0.01074392],"study_design_scores_gemma":[0.0001559621,0.0000120826,0.00003071364,0.00003223242,0.000005260244,0.000002290251,0.00003628423,0.7972726,0.0001775313,0.202156,0.00004761579,0.00007146291],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3759092,0.001699149,0.5957829,0.01407832,0.000367784,0.002878971,0.000002964873,0.0001849969,0.009095783],"genre_scores_gemma":[0.9674443,0.00001009483,0.03208214,0.0001917264,0.0000640833,0.0001836902,3.203106e-7,0.000009245906,0.00001440892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7950105,"threshold_uncertainty_score":0.255374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02817206264256897,"score_gpt":0.2770323464786412,"score_spread":0.2488602838360722,"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."}}