{"id":"W4402156018","doi":"10.1109/icc51166.2024.10622671","title":"Time Series Classification Using Convolutional Kernel and Adaptive Dynamic Thresholding","year":2024,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Thresholding; Series (stratigraphy); Kernel (algebra); Artificial intelligence; Pattern recognition (psychology); Time series; Machine learning; Mathematics; Image (mathematics)","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":[0.0001525727,0.00008370191,0.00009115621,0.00008388461,0.0001532683,0.0003086085,0.0001321913,0.00003205683,0.00007089136],"category_scores_gemma":[0.000007050474,0.00007058027,0.00003990266,0.0002876373,0.00007864272,0.000906778,0.0001298458,0.00006538716,0.0000408093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005336636,"about_ca_system_score_gemma":0.00004372386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001919776,"about_ca_topic_score_gemma":0.000006256407,"domain_scores_codex":[0.9992937,0.00001545911,0.0001367964,0.0002901701,0.0001263706,0.0001374604],"domain_scores_gemma":[0.9997224,0.00003800253,0.00002942729,0.0001289534,0.00004159019,0.0000396606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006666743,0.0000123106,0.0001671132,0.00002285227,0.00009349446,0.00001345854,0.000627268,0.001467561,0.0162529,0.9509569,0.0002649032,0.03011457],"study_design_scores_gemma":[0.00002589518,0.00001970767,0.00102551,0.0000281419,0.0000113578,0.000043508,0.000130188,0.9946225,0.00006755068,0.003384213,0.0005468212,0.00009460538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05284391,0.001013116,0.9407268,0.0007623641,0.0001359633,0.00006338092,0.000004169823,0.000257104,0.004193159],"genre_scores_gemma":[0.9369386,0.0000167642,0.06094581,0.00003120777,0.00003375682,0.000001917225,0.000003486034,0.000006319356,0.002022097],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9931549,"threshold_uncertainty_score":0.2975919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02875041972407858,"score_gpt":0.2515750271861781,"score_spread":0.2228246074620996,"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."}}