{"id":"W2126514643","doi":"10.1023/a:1019697023170","title":"Evaluation of Strong Consistency Web Caching Techniques","year":2002,"lang":"en","type":"article","venue":"World Wide Web","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; University of Wisconsin-Madison","keywords":"Computer science; Cache; Cache invalidation; Cache algorithms; Smart Cache; Distributed computing; Consistency (knowledge bases); Eventual consistency; Cache coherence; Causal consistency; Consistency model; Computer network; CPU cache; Data consistency; Sequential consistency","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01147773,0.0016409,0.001337533,0.001684699,0.00129922,0.0023693,0.00486065,0.001745843,0.002994925],"category_scores_gemma":[0.04489727,0.0007776713,0.0006936778,0.002922098,0.001698237,0.004732412,0.00221588,0.001556741,0.0004367805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0029788,"about_ca_system_score_gemma":0.004161612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00717339,"about_ca_topic_score_gemma":0.00614223,"domain_scores_codex":[0.9843616,0.005940155,0.0009686637,0.0009416718,0.006608482,0.001179287],"domain_scores_gemma":[0.918025,0.0536161,0.003156034,0.01342881,0.010241,0.00153308],"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":[0.0257678,0.006085764,0.03152137,0.002448737,0.001351555,0.0003897014,0.0006078455,0.361634,0.05877405,0.02370269,0.01521497,0.4725014],"study_design_scores_gemma":[0.001301806,0.002449005,0.005064674,0.0000469547,0.0004936799,0.0002511669,0.0002479993,0.9532639,0.02926399,0.005264867,0.002291101,0.00006090934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8608009,0.005716214,0.1119101,0.001049511,0.0004468493,0.0003893499,0.0005628084,0.005735012,0.01338924],"genre_scores_gemma":[0.9456563,0.0007222901,0.0506108,0.0001249599,0.0001473955,0.00006775815,0.0004458955,0.0002891288,0.001935448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01147773,"threshold_uncertainty_score":0.06070071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05286046055294654,"score_gpt":0.2679706923057275,"score_spread":0.215110231752781,"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."}}