{"id":"W2949258540","doi":"10.1186/s41235-019-0166-3","title":"Assessing the visual and cognitive demands of in-vehicle information systems","year":2019,"lang":"en","type":"article","venue":"Cognitive Research Principles and Implications","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"American Academy of Audiology Foundation; AAA Foundation for Traffic Safety","keywords":"Workload; Task (project management); Human–computer interaction; Computer science; Cognition; Variety (cybernetics); Motion (physics); Measure (data warehouse); Artificial intelligence; Engineering; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001328063,0.0005221298,0.0002116193,0.0007139746,0.0003480598,0.001083152,0.0004364496,0.0003048591,0.002951508],"category_scores_gemma":[0.008389981,0.0001741618,0.0002285537,0.0003473114,0.0002218621,0.0006414717,0.0006805193,0.0003095114,0.000345572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003960506,"about_ca_system_score_gemma":0.0003646262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002274286,"about_ca_topic_score_gemma":0.004064519,"domain_scores_codex":[0.9988999,0.0002669383,0.0001405979,0.0001215985,0.0004398719,0.000130975],"domain_scores_gemma":[0.991865,0.004014228,0.001496056,0.000277582,0.001687865,0.000659206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001541413,0.002387447,0.8445377,0.001381414,0.0003150002,0.0002812514,0.006657014,0.003275221,0.05459092,0.0002853121,0.001173194,0.08357405],"study_design_scores_gemma":[0.00002420285,0.001977454,0.9816721,0.00004914765,0.00006948706,0.0002429344,0.003121686,0.003587428,0.007761744,0.0002840296,0.001176346,0.00003346762],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977673,0.00005747656,0.0007245676,0.00001915954,0.00000352515,0.0000381335,0.00007678169,0.0000107218,0.001302284],"genre_scores_gemma":[0.9977919,0.00007073634,0.001536493,0.00001832197,0.000008178603,0.00004171925,0.0001540137,0.000004860954,0.0003738755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002951508,"threshold_uncertainty_score":0.009873748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1444990389873886,"score_gpt":0.5165949730363653,"score_spread":0.3720959340489767,"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."}}