{"id":"W4400046063","doi":"10.23977/jaip.2024.070219","title":"Analysis of artificial intelligence technology in electrical automation control","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Engineering and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automation; Field (mathematics); Artificial intelligence; Artificial neural network; Computer science; Deep learning; Control (management); Realization (probability); Engineering","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":[0.001358588,0.0001822092,0.0005241309,0.003288069,0.00002950137,0.00009078134,0.0003484258,0.0003396763,0.00004442473],"category_scores_gemma":[0.003300233,0.0001716356,0.0002011574,0.005558794,0.0001003364,0.0005584094,0.00002182842,0.00111931,0.00002778792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002072509,"about_ca_system_score_gemma":0.0001033103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002543739,"about_ca_topic_score_gemma":0.00001794614,"domain_scores_codex":[0.9977069,0.00006456936,0.001415957,0.0001687489,0.000343184,0.0003006325],"domain_scores_gemma":[0.9981624,0.001050078,0.0002465161,0.0002009226,0.0002904627,0.00004961949],"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":[0.00008180719,0.00008555804,0.00002391659,0.00002405567,0.0005828549,0.0001126732,0.0002434638,0.5396749,0.009768606,0.05345785,0.00001924284,0.3959251],"study_design_scores_gemma":[0.00001421221,0.0002208306,0.00002084842,0.00009092828,0.0006002295,0.00006527939,0.0010444,0.8974985,0.08613846,0.01359501,0.000552644,0.00015863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08259336,0.001944735,0.9117876,0.001781251,0.001154961,0.0001478631,0.000006864561,0.0003579803,0.0002253284],"genre_scores_gemma":[0.9949592,0.0003462593,0.004499631,0.0000104896,0.0001536165,0.000005697987,9.881826e-7,0.00002061507,0.000003470575],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9123659,"threshold_uncertainty_score":0.6999099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03023993130562038,"score_gpt":0.3101774300947645,"score_spread":0.2799374987891441,"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."}}