{"id":"W3094680994","doi":"10.47116/apjcri.2020.10.04","title":"Thick Data Analytics through Ensemble Techniques: Identifying Personalized EEG Biometrics based on Eye State Prediction","year":2020,"lang":"en","type":"article","venue":"Asia-pacific Journal of Convergent Research Interchange","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Analytics; Electroencephalography; Data analysis; Artificial intelligence; Pattern recognition (psychology); Data mining; Data science; Psychology; Neuroscience","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003945226,0.0003114796,0.0005029154,0.00139412,0.0002706528,0.0004409081,0.002369112,0.0001446629,0.0003648231],"category_scores_gemma":[0.00338363,0.0002559081,0.000228268,0.002748017,0.0004130848,0.001071131,0.0007116644,0.001713908,0.0001236949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002959211,"about_ca_system_score_gemma":0.000323747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002384211,"about_ca_topic_score_gemma":0.00000328009,"domain_scores_codex":[0.9934933,0.001282328,0.0009589746,0.0007796605,0.002693641,0.000792165],"domain_scores_gemma":[0.9963387,0.001116755,0.0004902449,0.0007966259,0.0007872511,0.0004704387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002623726,0.001028997,0.001692891,0.0007110287,0.0002619793,0.001395033,0.01552453,0.0001068623,0.729524,0.0007424076,0.2238303,0.02255822],"study_design_scores_gemma":[0.001402051,0.003860121,0.0000930633,0.0007922146,0.00004919216,0.00009828569,0.003923632,0.08269825,0.7088187,0.0008089378,0.1970659,0.0003896888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.311525,0.002124649,0.5699292,0.08557906,0.006904027,0.00355259,0.002030301,0.0006790494,0.01767609],"genre_scores_gemma":[0.9942961,0.001463244,0.002151903,0.0009630061,0.0003682982,0.000009660772,0.00002523913,0.00005267725,0.0006699195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.682771,"threshold_uncertainty_score":0.9999893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3614649791291497,"score_gpt":0.4251332200293913,"score_spread":0.06366824090024159,"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."}}