{"id":"W3167373986","doi":"10.1109/access.2021.3089660","title":"Driving Maneuver Classification Using Domain Specific Knowledge and Transfer Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Autoencoder; Artificial intelligence; Machine learning; Semi-supervised learning; Transfer of learning; Supervised learning; Classifier (UML); Domain knowledge; Encoder; Binary classification; Data modeling; Time series; Support vector machine; Feature vector; Deep learning; Pattern recognition (psychology); Artificial neural network","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":[],"consensus_categories":[],"category_scores_codex":[0.0002140623,0.0001072129,0.0001524472,0.00008009848,0.000294494,0.0006815618,0.000356609,0.00004833802,0.00004876891],"category_scores_gemma":[0.000007226836,0.0001062584,0.0000573415,0.0005471469,0.00003336238,0.0009285753,0.0001350314,0.0001500634,0.00001053809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000330047,"about_ca_system_score_gemma":0.0000346002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009236171,"about_ca_topic_score_gemma":0.00003083311,"domain_scores_codex":[0.9989729,0.00008943844,0.0002018206,0.0003970662,0.0001303406,0.000208422],"domain_scores_gemma":[0.9994804,0.00005966776,0.0000426337,0.0002586139,0.00009240866,0.00006628146],"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":[0.00001318042,0.0002282157,0.05973169,0.00015779,0.0001725447,0.0002240326,0.009599179,0.008824462,0.3893515,0.07092227,0.000623444,0.4601517],"study_design_scores_gemma":[0.000716587,0.00004317968,0.06452345,0.0001942501,0.00005570857,0.0001815737,0.0007378982,0.8084008,0.0312229,0.002383776,0.09068064,0.0008593114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5073094,0.0003389703,0.4904427,0.00007905799,0.000165435,0.00002638076,1.864145e-7,0.00004040062,0.001597496],"genre_scores_gemma":[0.9906647,0.00007777164,0.008785252,0.00002601136,0.0001361725,0.000002164524,0.000001782865,0.00001059947,0.0002955115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7995763,"threshold_uncertainty_score":0.6572316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06128118713335998,"score_gpt":0.2954326626828124,"score_spread":0.2341514755494524,"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."}}