{"id":"W3092074291","doi":"10.1016/j.dib.2020.106389","title":"Response time and eye tracking datasets for activities demanding varying cognitive load","year":2020,"lang":"en","type":"article","venue":"Data in Brief","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cognitive load; Alertness; Eye tracking; Automation; Machine learning; Task (project management); Cognition; Situation awareness; Artificial intelligence; BitTorrent tracker; Human–computer interaction; Support vector machine; Simulation; Engineering; Psychology; Systems engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009535767,0.00110832,0.0006801705,0.001988291,0.0006204362,0.0006554217,0.001221258,0.001663907,0.007955994],"category_scores_gemma":[0.004078636,0.0002254918,0.0009137988,0.001634189,0.0003402058,0.0004754941,0.001114264,0.001086898,0.009758736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007399652,"about_ca_system_score_gemma":0.000748392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009347925,"about_ca_topic_score_gemma":0.02626094,"domain_scores_codex":[0.9991535,0.0001147537,0.0001239838,0.0002209079,0.0002665847,0.0001202289],"domain_scores_gemma":[0.9965385,0.0005807038,0.0004093951,0.0008405113,0.001278349,0.0003525533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003170444,0.003556801,0.07461943,0.002200998,0.0004979681,0.0008393293,0.0007693764,0.004445619,0.01174641,0.001701601,0.7972755,0.09917665],"study_design_scores_gemma":[0.0007090195,0.00114198,0.6829414,0.0003118239,0.0001501899,0.001256442,0.0009087592,0.009156448,0.006380653,0.002421739,0.2943942,0.0002273691],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1330313,0.0004361924,0.002998457,0.0005202106,0.0002507351,0.0009257086,0.853099,0.002075596,0.006662719],"genre_scores_gemma":[0.06036656,0.0001903796,0.004853122,0.0002198236,0.00009772408,0.001685691,0.9282089,0.0001087641,0.004269058],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009347925,"threshold_uncertainty_score":0.02661544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09481893565208781,"score_gpt":0.4152899131182711,"score_spread":0.3204709774661832,"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."}}