{"id":"W4408358653","doi":"10.1109/tkde.2025.3550877","title":"Correlating Time Series With Interpretable Convolutional Kernels","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"U.S. Department of Energy","keywords":"Computer science; Series (stratigraphy); Kernel (algebra); Artificial intelligence; Convolutional neural network; Time series; Pattern recognition (psychology); Machine learning; 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":[0.000128501,0.0001314424,0.0001552535,0.0001439378,0.0001834114,0.0001286775,0.0003846102,0.00003826796,0.00002726996],"category_scores_gemma":[0.000005692806,0.0001165178,0.00002606145,0.000408851,0.00002831694,0.0008956365,0.00002238343,0.0001584922,0.00002692688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002398298,"about_ca_system_score_gemma":0.00004659882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009527855,"about_ca_topic_score_gemma":0.00001299086,"domain_scores_codex":[0.9992479,0.00001084055,0.0001586782,0.0003362025,0.00007130081,0.0001750475],"domain_scores_gemma":[0.9993111,0.00009659442,0.00002493876,0.0004736623,0.00004391855,0.00004981846],"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":[0.0001588787,0.0004774664,0.0003450486,0.0005071811,0.001417071,0.0000358273,0.002358376,0.5248614,0.00877963,0.03165643,0.002610949,0.4267918],"study_design_scores_gemma":[0.0001721849,0.00004831487,0.00009372883,0.0001673557,0.00003632692,0.0000192777,0.00002027839,0.9932309,0.001216679,0.00002544483,0.004826204,0.0001433275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001119236,0.0003730695,0.9966145,0.00006441461,0.0002176379,0.00005531346,0.00003137896,0.0001478094,0.001376628],"genre_scores_gemma":[0.9601921,0.00003336025,0.03595988,0.0000258963,0.00003133436,0.00001214987,0.00001632097,0.00001218317,0.003716748],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9606546,"threshold_uncertainty_score":0.4751457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00925635699884033,"score_gpt":0.2212340470260673,"score_spread":0.2119776900272269,"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."}}