{"id":"W4413247582","doi":"10.1080/03610926.2025.2538532","title":"On a spectral density estimator based similarity test for correlated time series","year":2025,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Series (stratigraphy); Estimator; Similarity (geometry); Mathematics; Statistics; Time series; Pattern recognition (psychology); Artificial intelligence; Computer science; Geology","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":[0.003192038,0.0001192973,0.000238509,0.0001181625,0.0003536279,0.0001120062,0.0004568591,0.00006425117,0.00002259664],"category_scores_gemma":[0.003594759,0.0001149766,0.00003662981,0.0003258497,0.0001647496,0.0001383347,0.0001843894,0.0001834719,0.000002347736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003664355,"about_ca_system_score_gemma":0.00006579005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001027235,"about_ca_topic_score_gemma":0.00001453041,"domain_scores_codex":[0.9982686,0.001002664,0.0002855392,0.0002295942,0.00005724635,0.0001563727],"domain_scores_gemma":[0.9902694,0.008757976,0.0001163529,0.0007021358,0.0001121829,0.00004195217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009455979,0.00006552253,0.0003071286,0.00002802078,0.00001502703,7.933293e-7,0.0001738684,0.0003225626,0.000197071,0.9497561,0.0002498989,0.04878943],"study_design_scores_gemma":[0.0002922114,0.00007196334,0.003045727,0.00006642177,0.00002440134,0.000001275989,0.00002928682,0.4638727,0.0008514442,0.531105,0.0005397835,0.0000997863],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007277075,0.0001771382,0.997152,0.00042269,0.00005606406,0.0001839868,0.00003766642,0.0000554239,0.001187365],"genre_scores_gemma":[0.06612614,0.00002090386,0.932954,0.0002642575,0.000003608961,0.00002714986,0.00002860566,0.000005614116,0.0005697416],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4635501,"threshold_uncertainty_score":0.4688608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01729355380501326,"score_gpt":0.350921363602519,"score_spread":0.3336278097975057,"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."}}