{"id":"W7094941570","doi":"10.5683/sp3/h0aelt","title":"CLARE: Cognitive Load Assessment in REaltime with Multimodal Data","year":2023,"lang":"","type":"dataset","venue":"Borealis","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Cognitive load; Cognition; Gaze; Cognitive Assessment System; Data collection","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001642511,0.001027339,0.0009432599,0.0002389707,0.0002044102,0.0002111218,0.002367095,0.001312405,0.00006145114],"category_scores_gemma":[0.001295359,0.0009506289,0.0001225515,0.000464556,0.0004161866,0.00002898073,0.002901589,0.001937905,0.0002348154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002360174,"about_ca_system_score_gemma":0.002699174,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07758338,"about_ca_topic_score_gemma":0.120033,"domain_scores_codex":[0.9944438,0.0004526189,0.001249717,0.001689463,0.001100514,0.001063897],"domain_scores_gemma":[0.9941916,0.0002154856,0.0009417398,0.00389577,0.0004296542,0.0003257849],"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":[0.000529146,0.0003603529,0.001307072,0.0005776813,0.0005311027,0.0002519062,0.0001054784,0.0001596762,0.00004063355,0.000006911137,0.9910806,0.005049409],"study_design_scores_gemma":[0.00377075,0.001408948,0.009913979,0.0009702488,0.0004237388,0.00008503002,0.0005019963,0.0178753,0.00005642655,0.000007435113,0.9635513,0.001434893],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003017917,0.0001993681,0.0008496086,0.0003771491,0.0002032006,0.001285536,0.9945477,0.0000423576,0.00219335],"genre_scores_gemma":[0.001522068,0.002779674,0.003083118,0.0005572445,0.0005107113,0.0001291682,0.9909654,0.0001464672,0.0003061105],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04244961,"threshold_uncertainty_score":0.9999841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02107162470859449,"score_gpt":0.3318497821119049,"score_spread":0.3107781574033104,"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."}}