{"id":"W2158254211","doi":"10.2307/3316074","title":"Detection of patterns in noisy time series","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Series (stratigraphy); Context (archaeology); White noise; Noise (video); Binary number; Statistics; Independent and identically distributed random variables; Algorithm; Term (time); Mathematics; Key (lock); Time series; Computer science; Amplitude; Artificial intelligence; Geography; Random variable; Arithmetic; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001119141,0.0003338171,0.0005877643,0.002197404,0.0002302078,0.0009226376,0.0003877649,0.0005175395,0.0008627911],"category_scores_gemma":[0.01233172,0.0002120369,0.0002097641,0.001832087,0.0004514948,0.0007387175,0.0004635327,0.0004620804,0.0002913734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002345255,"about_ca_system_score_gemma":0.0001921982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008193639,"about_ca_topic_score_gemma":0.0004694043,"domain_scores_codex":[0.9991937,0.0001658182,0.00008555542,0.0002030689,0.0002766054,0.00007533811],"domain_scores_gemma":[0.9945315,0.00271578,0.001219536,0.0004918606,0.0008466961,0.0001946249],"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":[0.003010656,0.0004783599,0.2606518,0.0007368881,0.0005735257,0.002941147,0.001598088,0.08925062,0.1659389,0.01560272,0.005754035,0.4534633],"study_design_scores_gemma":[0.00005346494,0.0003773406,0.3224718,0.00008158831,0.0001270739,0.001266212,0.0004876311,0.6201499,0.03188232,0.01836205,0.004648396,0.00009228077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8732905,0.0004307596,0.1225557,0.0002716601,0.0001026946,0.00004256686,0.0006872869,0.0006295191,0.001989326],"genre_scores_gemma":[0.9858316,0.0001175247,0.01308363,0.0000162979,0.00005953435,0.0000199352,0.0005309478,0.00002332453,0.0003170882],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002197404,"threshold_uncertainty_score":0.005918622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009874937952134746,"score_gpt":0.1870694426751647,"score_spread":0.17719450472303,"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."}}