{"id":"W2306172304","doi":"10.1109/tsmcc.2010.2052041","title":"Optimizing Operator–Agent Interaction in Intelligent Adaptive Interface Design: A Conceptual Framework","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University; Defence Research and Development Canada","funders":"","keywords":"Interface (matter); Workload; Sociotechnical system; Computer science; Conceptual design; Systems engineering; User interface; Operator (biology); Control (management); Intelligent agent; Conceptual framework; Human–computer interaction; Knowledge management; Engineering; Artificial intelligence","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005402679,0.0003057681,0.000446367,0.0002317993,0.0002787917,0.0001455328,0.0001542547,0.0002496995,0.001188705],"category_scores_gemma":[0.000009905472,0.0002722531,0.0000975193,0.0002564808,0.0001970971,0.0001358041,0.000005344243,0.0008640115,0.0007659269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000674907,"about_ca_system_score_gemma":0.00002282536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009982718,"about_ca_topic_score_gemma":0.0001876772,"domain_scores_codex":[0.9977709,0.0003640634,0.0008921115,0.000563763,0.0001449306,0.0002642408],"domain_scores_gemma":[0.9986475,0.0002836391,0.000253615,0.0005077767,0.0001032879,0.0002041903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009238349,0.004669371,0.0002500082,0.0006478797,0.0009794632,0.00001286113,0.08634903,0.02020281,0.009719433,0.2186551,0.01291355,0.6446766],"study_design_scores_gemma":[0.0005070789,0.0001930516,0.0001116155,0.0005471279,0.00008285162,0.0001139657,0.005257657,0.003182748,0.0004460232,0.0001076958,0.9889809,0.0004692204],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008849932,0.003289383,0.9811988,0.0001973557,0.002079959,0.00200501,0.00002719252,0.00008841352,0.002263939],"genre_scores_gemma":[0.9836686,0.004512288,0.004827323,0.0002277785,0.0001716959,0.002373042,0.000007151665,0.00003851656,0.004173588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9763715,"threshold_uncertainty_score":0.9999729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08370662716168095,"score_gpt":0.3698764251082972,"score_spread":0.2861697979466162,"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."}}