{"id":"W4414860688","doi":"10.36227/techrxiv.175979160.00083957/v1","title":"Benchmarking Deep Legendre-SNN for Time Series Classification - Analysis and Enhancements","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"Division of Electrical, Communications and Cyber Systems; National Science Foundation","keywords":"Benchmark (surveying); Univariate; Benchmarking; Time series; Multivariate statistics; Series (stratigraphy); Neuromorphic engineering; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000413838,0.0002712341,0.0005445272,0.0004830817,0.0002705817,0.0005867938,0.0007161201,0.0001723435,0.0001254447],"category_scores_gemma":[0.00002900959,0.0002560204,0.0003334174,0.0008148307,0.00004176479,0.0003537233,0.001314655,0.000154381,0.000005412261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000607801,"about_ca_system_score_gemma":0.00007309733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007637571,"about_ca_topic_score_gemma":0.0001834823,"domain_scores_codex":[0.9979976,0.00004583887,0.0004992854,0.0009476359,0.0002223316,0.0002873469],"domain_scores_gemma":[0.9985008,0.00008947193,0.0003490567,0.0007931313,0.0001965946,0.00007095972],"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":[0.00004425557,0.000148353,0.005215924,0.0006778769,0.008391795,0.000003412674,0.001936377,0.009426271,0.001277068,0.1211416,0.001285756,0.8504513],"study_design_scores_gemma":[0.00008905307,0.00003671307,0.002734858,0.00003884701,0.0007812724,5.776523e-7,0.00004731827,0.9899117,0.0006832267,0.002368074,0.002994202,0.0003141154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001054647,0.0002311488,0.9896634,0.0004532602,0.0002052269,0.0003027607,0.00002906866,0.0001105095,0.007949932],"genre_scores_gemma":[0.1597959,0.0003122737,0.8106294,0.0002238247,0.0002583025,0.0002645903,0.0006577434,0.00001805464,0.02783993],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9804855,"threshold_uncertainty_score":0.9999892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01865274969480761,"score_gpt":0.2609492985816652,"score_spread":0.2422965488868575,"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."}}