{"id":"W2555777152","doi":"10.1002/acs.2696","title":"A semi‐adaptive control approach to closed‐loop medication infusion","year":2016,"lang":"en","type":"article","venue":"International Journal of Adaptive Control and Signal Processing","topic":"Anesthesia and Sedative Agents","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Control theory (sociology); Controller (irrigation); Adaptive control; Sensitivity (control systems); Fidelity; Computer science; Closed loop; Identification (biology); Control engineering; Reference model; System identification; Set (abstract data type); Control (management); Engineering; Artificial intelligence; Data modeling","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005651167,0.0001876992,0.0004280443,0.0002911475,0.00008449297,0.0000425625,0.0001985229,0.00009232506,0.00004550501],"category_scores_gemma":[0.0001974507,0.0001109727,0.0001198582,0.00009569059,0.0001344448,0.0004144363,0.00002640679,0.0002028415,0.000009773788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001433376,"about_ca_system_score_gemma":0.0002611524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007504934,"about_ca_topic_score_gemma":6.481949e-7,"domain_scores_codex":[0.9980975,0.0001085287,0.0005501039,0.0002297505,0.0008174614,0.0001966985],"domain_scores_gemma":[0.9973632,0.0001930056,0.0005066994,0.00006085857,0.001595772,0.0002805055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01788715,0.0008099692,0.01726015,0.0000388448,0.001298015,0.0003876095,0.00187091,0.00009371622,0.2155338,0.002811695,0.000957423,0.7410507],"study_design_scores_gemma":[0.1330688,0.01628011,0.6763321,0.01026089,0.00261167,0.008823797,0.006206044,0.1029649,0.01076597,0.01187124,0.01864287,0.002171607],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1771918,0.0007618131,0.8127428,0.007014209,0.0001104668,0.0003442572,0.00001599884,0.00001693059,0.001801736],"genre_scores_gemma":[0.9944165,0.00004982826,0.001928076,0.002667494,0.0006129766,0.00001125728,0.000002535266,0.00001692137,0.0002943496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8172247,"threshold_uncertainty_score":0.4525337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02151142056949458,"score_gpt":0.2730277933615533,"score_spread":0.2515163727920587,"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."}}