{"id":"W1988109548","doi":"10.1007/s10489-008-0144-9","title":"STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases","year":2008,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Noise (video); Suffix tree; Algorithm; Series (stratigraphy); Suffix; Tree (set theory); Sequence (biology); Time series; Symbol (formal); Data structure; Data mining; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001793176,0.0007875961,0.001239787,0.002282771,0.0007917963,0.001153886,0.001468051,0.001185585,0.003129519],"category_scores_gemma":[0.006320333,0.0004067774,0.0006556004,0.002653005,0.0005450758,0.001967613,0.00107149,0.001066215,0.002306598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003574743,"about_ca_system_score_gemma":0.001059378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001294356,"about_ca_topic_score_gemma":0.001683698,"domain_scores_codex":[0.9987842,0.0002464613,0.0001699086,0.0002333037,0.0004978409,0.00006830336],"domain_scores_gemma":[0.9974842,0.0009873519,0.0002265301,0.0006448571,0.0005725797,0.00008446497],"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":[0.00116099,0.0002090399,0.001721334,0.0002210154,0.0001222949,0.0002393023,0.0001233053,0.02581949,0.03638722,0.005564684,0.00960446,0.9188269],"study_design_scores_gemma":[0.0001303775,0.0004699856,0.001629327,0.00004306281,0.0000764832,0.0008253398,0.00007682313,0.9221978,0.04901906,0.012948,0.01252745,0.00005631747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02115489,0.0007074921,0.9717657,0.0001854292,0.0001740837,0.00008660917,0.000533,0.004750765,0.0006420274],"genre_scores_gemma":[0.1142282,0.0003972151,0.8802907,0.0001956712,0.0001759524,0.0001642737,0.001784527,0.0003155537,0.002447958],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003129519,"threshold_uncertainty_score":0.01046926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02542355260735932,"score_gpt":0.253906476987624,"score_spread":0.2284829243802647,"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."}}