{"id":"W1632814704","doi":"10.1109/dcc.2003.1194060","title":"Pattern matching by means of multi-resolution compression","year":2003,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pattern matching; Computer science; Compression (physics); Matching (statistics); Data compression; Scheme (mathematics); Compression ratio; Algorithm; Artificial intelligence; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007992743,0.0008340966,0.001194149,0.003328163,0.0006980281,0.001848917,0.001877784,0.0008316871,0.0341818],"category_scores_gemma":[0.005410579,0.0003420048,0.0007941126,0.006509337,0.0006472841,0.002972635,0.001406766,0.0008899901,0.01267268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006005456,"about_ca_system_score_gemma":0.0006004654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001189024,"about_ca_topic_score_gemma":0.001254274,"domain_scores_codex":[0.9988021,0.0001541105,0.0001466269,0.000313784,0.0005042013,0.00007922087],"domain_scores_gemma":[0.9974188,0.000619891,0.000145438,0.001051244,0.0007004785,0.0000642322],"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":[0.0002132941,0.00004854316,0.000475449,0.000422086,0.00006737596,0.0003565509,0.00008740118,0.007478387,0.02563802,0.01573134,0.02064749,0.928834],"study_design_scores_gemma":[0.0001788966,0.0004277512,0.003633123,0.0002754715,0.0002600479,0.005000148,0.000320608,0.4095147,0.2296729,0.06534951,0.2851757,0.000191176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008854921,0.001456043,0.9753457,0.000368778,0.0005205676,0.0002426333,0.0009460746,0.004378259,0.007887043],"genre_scores_gemma":[0.07936074,0.00163017,0.8966112,0.0002155908,0.0003448516,0.0003123116,0.003788034,0.0008417782,0.01689542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0341818,"threshold_uncertainty_score":0.1143495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890905317796621,"score_gpt":0.2530720331481034,"score_spread":0.2341629799701372,"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."}}