{"id":"W4387379921","doi":"10.1049/ccs2.12088","title":"On validating a generic camera‐based blink detection system for cognitive load assessment","year":2023,"lang":"en","type":"article","venue":"Cognitive Computation and Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Workload; Cognitive load; Computer science; Metric (unit); Task (project management); Cognition; Eye tracking; Human–computer interaction; Simulation; Artificial intelligence; Engineering; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007454644,0.0002079819,0.0002908914,0.0003664173,0.000429408,0.0001733187,0.00004181151,0.000117775,0.00006819319],"category_scores_gemma":[0.0002134716,0.0002077209,0.00009619427,0.0003985111,0.00004422011,0.00009220142,0.00001465068,0.0001560099,0.0005054181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001938435,"about_ca_system_score_gemma":0.00007374983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004274289,"about_ca_topic_score_gemma":0.00000802754,"domain_scores_codex":[0.9979109,0.0005469691,0.0005160705,0.0004704971,0.0003018554,0.0002536859],"domain_scores_gemma":[0.9965342,0.002018863,0.0003506615,0.00006979826,0.0009226154,0.0001038565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003650613,0.001177237,0.003620509,0.003632079,0.0023031,0.0001917063,0.02689652,0.0179337,0.005672547,0.02687915,0.01216757,0.8958753],"study_design_scores_gemma":[0.005173639,0.0005956129,0.01297813,0.0008089026,0.0001045785,0.00003796361,0.03303233,0.9459469,0.0003829225,0.0000556347,0.0005137361,0.0003696369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2803965,0.00003271182,0.7026408,0.00006325444,0.002917441,0.001408964,0.0001456768,0.0005599717,0.01183465],"genre_scores_gemma":[0.9972542,0.000001225262,0.00006500025,0.000287085,0.0002350107,0.000944935,0.0003636027,0.00003535527,0.0008135948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9280132,"threshold_uncertainty_score":0.8470611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09360486721002552,"score_gpt":0.4193054562362858,"score_spread":0.3257005890262603,"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."}}