{"id":"W3217615881","doi":"10.3390/app112210957","title":"Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Subspace topology; Artificial intelligence; Machine learning; Classifier (UML); Raw data; Convolutional neural network; Random subspace method; Series (stratigraphy); Construct (python library); Pattern recognition (psychology); Data mining","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":[0.0009884415,0.0007804166,0.0007811792,0.0006722141,0.0003020076,0.0004210331,0.0008126476,0.0006028354,0.0007239942],"category_scores_gemma":[0.001686191,0.0002882853,0.0007532702,0.0007924621,0.0002660486,0.0009107587,0.0005849238,0.0008228249,0.0003056308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005687659,"about_ca_system_score_gemma":0.0007732615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008745347,"about_ca_topic_score_gemma":0.009476198,"domain_scores_codex":[0.9994912,0.000155031,0.00002726808,0.0001192161,0.0001381635,0.00006904243],"domain_scores_gemma":[0.9994878,0.0001559437,0.00005796457,0.00009238804,0.0001650283,0.00004101646],"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":[0.0001513359,0.0001210418,0.00312038,0.00005845735,0.0001996775,0.00007908404,0.00005951069,0.6183072,0.006742191,0.006792142,0.004499996,0.359869],"study_design_scores_gemma":[0.000001260375,0.00001151489,0.0001668419,0.000001826162,0.000006005722,0.00000694277,0.000002394559,0.9982533,0.0004611827,0.0008608422,0.0002246057,0.000003261843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08821669,0.001931677,0.9055609,0.0002779151,0.000121804,0.00004804794,0.0002300877,0.001404247,0.002208611],"genre_scores_gemma":[0.8316378,0.0007939331,0.1626885,0.0001599975,0.0001235502,0.0001071053,0.0008606801,0.00008605229,0.003542483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008745347,"threshold_uncertainty_score":0.01738888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02366369401184197,"score_gpt":0.2334169320568871,"score_spread":0.2097532380450451,"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."}}