{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006991216,0.000756084,0.0005365098,0.0008591871,0.0002790135,0.0005300951,0.0008705528,0.0009276373,0.0008216301],"category_scores_gemma":[0.002393331,0.0002117953,0.0007054668,0.0005434635,0.0004320212,0.001458685,0.0006747748,0.0009883135,0.0003918223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006329236,"about_ca_system_score_gemma":0.000605787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00505115,"about_ca_topic_score_gemma":0.00358314,"domain_scores_codex":[0.9995738,0.00009701846,0.00002546712,0.0001590428,0.00008260063,0.00006213055],"domain_scores_gemma":[0.9989821,0.0004345737,0.0001086386,0.0001434579,0.0002839039,0.00004730138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002918195,0.0007569788,0.01078595,0.00009218136,0.0001776551,0.0003056217,0.0002315656,0.5147098,0.008987238,0.001564596,0.002602151,0.4594944],"study_design_scores_gemma":[0.000002676621,0.00002601692,0.000912559,0.000002921574,0.000006646286,0.00001429845,0.00001707361,0.9967164,0.001160519,0.00103671,0.00009871675,0.000005225213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5644711,0.0007434596,0.4267337,0.0004758051,0.0001086966,0.0001299743,0.0003408499,0.002413691,0.00458278],"genre_scores_gemma":[0.9837272,0.00008789376,0.01462696,0.00007205397,0.00002541475,0.00004002422,0.0003510245,0.00001682535,0.001052468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00505115,"threshold_uncertainty_score":0.01004344,"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."}}