{"id":"W1966651940","doi":"10.1109/noms.2006.1687551","title":"Distributed Pattern Matching for P2P Systems","year":2006,"lang":"en","type":"article","venue":"","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Distributed hash table; Distributed computing; Search engine indexing; Matching (statistics); Service discovery; Overhead (engineering); Theoretical computer science; Replication (statistics); Peer-to-peer; Information retrieval; Web service","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":[0.002051842,0.0006008315,0.0009916357,0.0008275325,0.001316353,0.003026979,0.001735708,0.001759237,0.007095327],"category_scores_gemma":[0.007413586,0.000559208,0.0006992525,0.002010744,0.001076764,0.00449479,0.002685777,0.001555977,0.002429158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001627053,"about_ca_system_score_gemma":0.001641231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002039008,"about_ca_topic_score_gemma":0.001306677,"domain_scores_codex":[0.997382,0.0007639978,0.0002529548,0.0004750953,0.0009627335,0.0001632155],"domain_scores_gemma":[0.9979486,0.0007692824,0.0001297368,0.00072949,0.0003340658,0.00008884583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002420805,0.0001220648,0.0008275813,0.0006596685,0.00008258727,0.0005991599,0.0002889733,0.1081088,0.009543172,0.5034128,0.02511152,0.3510016],"study_design_scores_gemma":[0.0001338561,0.00008282348,0.0002483187,0.00005381046,0.00003189916,0.0005752386,0.00008887469,0.4983931,0.004530096,0.4208197,0.07500637,0.00003598325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005389362,0.001060613,0.9803925,0.001108366,0.0001802781,0.0002998501,0.0001801868,0.002274036,0.009114769],"genre_scores_gemma":[0.2697391,0.002093047,0.7122265,0.0004252672,0.0002711985,0.0008946803,0.0008317349,0.0003182979,0.0132003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007095327,"threshold_uncertainty_score":0.02373624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01175980384258283,"score_gpt":0.2284900371823163,"score_spread":0.2167302333397335,"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."}}