{"id":"W3155043239","doi":"10.5430/air.v10n1p43","title":"Development process of multiagent system for glycemic control of intensive care unit patients","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Glycemic; Intensive care unit; Process (computing); Medicine; Health care; Intensive care medicine; Control (management); Inference; Computer science; Risk analysis (engineering); Process management; Engineering; Artificial intelligence; Diabetes mellitus","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":[0.000695998,0.0001305086,0.0003103792,0.0001960229,0.0001935125,0.00005780165,0.000856158,0.00008811883,0.00001291556],"category_scores_gemma":[0.001922099,0.0001152715,0.00009112954,0.0008373521,0.0001691072,0.0001336248,0.0002737945,0.000180299,0.00004143127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001376724,"about_ca_system_score_gemma":0.000895889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004622557,"about_ca_topic_score_gemma":0.00008289117,"domain_scores_codex":[0.9975706,0.0001832,0.000616796,0.0004467968,0.0006953721,0.0004872862],"domain_scores_gemma":[0.981464,0.0005168475,0.000153904,0.0004244177,0.01731938,0.0001214756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001129142,0.001085049,0.01020744,0.004094194,0.0002582008,0.00005208551,0.1230248,0.001003919,0.02229263,0.2466888,0.0001393981,0.5900244],"study_design_scores_gemma":[0.0002148558,0.0003580993,0.0002278824,0.0002900247,0.00000776413,0.000002069296,0.04636435,0.01975657,0.9307863,0.001456156,0.0003772023,0.0001587126],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4812424,0.0005594186,0.5157827,0.00007910444,0.0004572225,0.0009794137,0.00001903471,0.00004288238,0.0008377968],"genre_scores_gemma":[0.9964179,0.000009589997,0.003349837,0.00001670468,0.00003896391,0.0001047859,0.0000119984,0.00001026874,0.00003991445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9084937,"threshold_uncertainty_score":0.4700636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1556144380074251,"score_gpt":0.399868837379012,"score_spread":0.2442543993715869,"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."}}