{"id":"W2752020793","doi":"10.1007/s00521-017-3173-7","title":"pART2: using adaptive resonance theory for web caching prefetching","year":2017,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; BAH Enterprises (Canada); Trent University","funders":"Natural Sciences and Engineering Research Council of Canada; Trent University","keywords":"Computer science; Cache; Scheme (mathematics); Cluster analysis; CPU cache; Monte Carlo method; Sensitivity (control systems); Data mining; Distributed computing; Parallel computing; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003905912,0.0001199089,0.0001340129,0.00003758954,0.00241487,0.0004537589,0.0006512318,0.00003639748,2.030957e-7],"category_scores_gemma":[0.00005104503,0.0001146174,0.00006138133,0.00004475738,0.00006421888,0.000300815,0.0003357333,0.0001615242,0.000001503135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001839471,"about_ca_system_score_gemma":0.00002898975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005457548,"about_ca_topic_score_gemma":0.000005850153,"domain_scores_codex":[0.9990564,0.00005362173,0.0001611232,0.0004040381,0.00009513384,0.0002296868],"domain_scores_gemma":[0.9987995,0.0002915679,0.00017779,0.0006048718,0.0000596449,0.00006664325],"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":[0.00002321681,0.00005152213,0.001790176,0.00002985743,0.00002360526,0.000002247576,0.0004533115,0.006126664,0.008475455,0.3385528,0.0001565315,0.6443146],"study_design_scores_gemma":[0.0002464126,0.00002663202,0.002492994,0.00004490067,0.00001180381,0.00001735732,0.00003381238,0.9879285,0.00009040729,0.006942097,0.002006138,0.0001589777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.252863,0.0004286013,0.7453124,0.000436182,0.000117287,0.0002932597,0.000006964995,0.0001473718,0.0003949296],"genre_scores_gemma":[0.9882941,0.000006117932,0.01107537,0.0002564443,0.0002653308,0.00002875017,0.000001359791,0.00000959405,0.00006296164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9818018,"threshold_uncertainty_score":0.9988838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05780522400887275,"score_gpt":0.3106713973151593,"score_spread":0.2528661733062866,"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."}}