{"id":"W3195584230","doi":"10.1109/thms.2021.3107675","title":"Individualized Mutual Adaptation in Human-Agent Teams","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Adaptation (eye); Computer science; Task (project management); Metric (unit); Knowledge management; Artificial intelligence; Human–computer interaction; Process management; Psychology; Business; Engineering; Marketing","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":["insufficient_payload"],"category_scores_codex":[0.0005830548,0.0003821857,0.000534496,0.000673208,0.0005759815,0.0001576973,0.0002492599,0.0002751923,0.01141892],"category_scores_gemma":[0.00001298822,0.0004042738,0.000270435,0.0005381315,0.00007156925,0.0002607142,0.000002947919,0.0007910169,0.001401941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003589116,"about_ca_system_score_gemma":0.00007894275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002157541,"about_ca_topic_score_gemma":0.003350844,"domain_scores_codex":[0.9960791,0.0009970549,0.00122095,0.000711556,0.0005388177,0.0004524921],"domain_scores_gemma":[0.9984178,0.0002331836,0.0002952651,0.0007113708,0.0001757897,0.000166582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001545225,0.02300741,0.002824075,0.001087012,0.004437135,0.002023045,0.2234427,0.3994615,0.05343537,0.1785402,0.03259075,0.07760549],"study_design_scores_gemma":[0.08614203,0.005213122,0.05958544,0.003900961,0.001565217,0.00292509,0.2043495,0.125607,0.02701344,0.002270806,0.4700359,0.01139153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4577017,0.0005808577,0.3742301,0.0005067378,0.01630098,0.001923283,0.0005607465,0.001202578,0.146993],"genre_scores_gemma":[0.971002,0.000009914341,0.00008096805,0.0002172181,0.0002014656,0.0004390908,0.0001748889,0.00006919655,0.02780521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5133003,"threshold_uncertainty_score":0.9998409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06216651657609476,"score_gpt":0.3856208231288542,"score_spread":0.3234543065527594,"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."}}