{"id":"W1534909283","doi":"10.3233/mgs-120198","title":"FRIENDs: Brain-monitoring agents for adaptive socio-technical systems","year":2013,"lang":"en","type":"article","venue":"Multiagent and Grid Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Context (archaeology); Ubiquitous computing; Human–computer interaction; Domain (mathematical analysis); Field (mathematics); Architecture; Overhead (engineering); Data science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003310786,0.0002802957,0.0003750472,0.00009338485,0.0003428394,0.0004575001,0.0003469331,0.0001513698,0.00001225693],"category_scores_gemma":[0.0001409488,0.0002250518,0.0001133131,0.0001104557,0.00009614696,0.0003240953,0.0001450147,0.0001728887,0.0001102963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006410554,"about_ca_system_score_gemma":0.00001754056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002462734,"about_ca_topic_score_gemma":0.000001480323,"domain_scores_codex":[0.9978382,0.0001889358,0.0004752921,0.0006488721,0.0003402554,0.000508435],"domain_scores_gemma":[0.9984703,0.0007640612,0.000186719,0.0002844834,0.00009357931,0.0002008468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001506605,0.0005453837,0.01274243,0.001622084,0.0002071889,0.00008079023,0.007312014,0.0028966,0.7192501,0.00495036,0.2447396,0.005502867],"study_design_scores_gemma":[0.008224348,0.002426367,0.01539577,0.002683226,0.0001832929,0.0005475778,0.02006291,0.4577576,0.1666714,0.0002444828,0.3221687,0.003634287],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732034,0.001573759,0.008924562,0.0005450133,0.01114981,0.003321941,0.0001091945,0.0003855954,0.0007867882],"genre_scores_gemma":[0.9952387,0.00003365476,0.0001851354,0.0001091241,0.001566611,0.0005948762,0.000004385772,0.00003570991,0.002231789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5525786,"threshold_uncertainty_score":0.9177347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07479059236616932,"score_gpt":0.3054931014825321,"score_spread":0.2307025091163628,"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."}}