{"id":"W2806604911","doi":"10.1007/978-3-319-93000-8_50","title":"Transfer Learning Based Strategy for Improving Driver Distraction Recognition","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Distraction; Computer science; Robustness (evolution); Artificial intelligence; Transfer of learning; Machine learning; Phone; The Internet; Distracted driving","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"],"consensus_categories":[],"category_scores_codex":[0.0005256552,0.0004446946,0.0003441497,0.0004516181,0.000417476,0.0004770619,0.0007771163,0.0003147083,0.0001459903],"category_scores_gemma":[0.0002041838,0.0004092755,0.000162187,0.00022055,0.0006928485,0.00064857,0.0001033899,0.0007885764,0.00004698284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001809507,"about_ca_system_score_gemma":0.0002608778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001080274,"about_ca_topic_score_gemma":0.00003702457,"domain_scores_codex":[0.9969423,0.00005371164,0.000422668,0.001471725,0.0005471401,0.0005624963],"domain_scores_gemma":[0.9982097,0.0009627959,0.0001718125,0.0003538306,0.0001907028,0.0001111756],"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":[0.00007142458,0.0000435483,0.000007728887,0.0001472346,0.000004009137,0.00002385926,0.0003657026,0.06746651,0.09285963,0.0002063401,0.00001691786,0.8387871],"study_design_scores_gemma":[0.0005548879,0.0009709726,0.00002663821,0.000412573,0.0000224239,0.00004196874,7.637198e-7,0.572331,0.4019603,0.0210835,0.001827619,0.0007673603],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.012881,0.00002255401,0.9838077,0.0001762849,0.001534642,0.0005715598,0.00003225483,0.000160495,0.0008134965],"genre_scores_gemma":[0.9765241,0.000006090034,0.02077505,0.001174358,0.000959504,0.00002024195,0.00002353542,0.00005873926,0.0004584275],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9636431,"threshold_uncertainty_score":0.9998359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04392875312811714,"score_gpt":0.2733580655882872,"score_spread":0.22942931246017,"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."}}