{"id":"W4399478796","doi":"10.54941/ahfe1004735","title":"NeuroTeaming: Using Power Spectral Density for Adjusting Teaming Dynamics in Pilot-AI Task Allocation","year":2024,"lang":"en","type":"article","venue":"AHFE international","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Thales (Canada)","funders":"","keywords":"Computer science; Task (project management); Transparency (behavior); Interdependence; Situation awareness; Electroencephalography; Task analysis; Artificial intelligence; Human–computer interaction; Obstacle avoidance; Identification (biology); Obstacle; Engineering; Computer security; Robot; Psychology; Systems engineering; Mobile robot","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003206947,0.0001396938,0.0001219611,0.0003777377,0.00008569784,0.0001527585,0.0001766121,0.00007368347,0.001258127],"category_scores_gemma":[0.0001762183,0.0001566092,0.00008854291,0.0001558344,0.00003290096,0.0003667393,0.00003808626,0.0002985589,0.0001703972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006579801,"about_ca_system_score_gemma":0.00005749326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002634926,"about_ca_topic_score_gemma":0.000342251,"domain_scores_codex":[0.9987156,0.00005912093,0.0004288972,0.0003664163,0.0002057467,0.0002242003],"domain_scores_gemma":[0.9993321,0.0002412912,0.00008944095,0.0001405694,0.0001531682,0.00004344687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005231482,0.0007038317,0.02431221,0.0001058639,0.0004720782,0.0001849843,0.008953226,0.003457526,0.01761771,0.9056274,0.009923421,0.02811866],"study_design_scores_gemma":[0.0008314678,0.00011684,0.04965314,0.0001822635,0.00002558339,0.0001974027,0.001366742,0.928412,0.0002686805,0.002530046,0.01610228,0.0003135558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8169171,0.00004108136,0.1433488,0.003180656,0.009599161,0.0003630575,0.0000503483,0.0002594673,0.02624033],"genre_scores_gemma":[0.9948148,0.000001448058,0.001114087,0.0006560931,0.0004123137,0.00003539969,0.0001063558,0.00003213571,0.002827399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9249545,"threshold_uncertainty_score":0.9996548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03924021624879075,"score_gpt":0.3885380874000817,"score_spread":0.3492978711512909,"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."}}