{"id":"W4246345223","doi":"10.1109/cns.2013.6682676","title":"Program","year":2013,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Army; Army Research Laboratory; Télécom Paris; University of Illinois at Urbana-Champaign; National Institute of Informatics; National Institute of Standards and Technology; Università di Pisa; University of British Columbia; Kungliga Tekniska Högskolan; Tsinghua University; Bar-Ilan University","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002306027,0.00002421647,0.00002313417,0.00001393076,0.00002189685,0.0001379886,0.0002327801,0.000008386612,0.00006430475],"category_scores_gemma":[0.000002868582,0.00001760451,0.00001809749,0.00005578724,0.000004715199,0.0002330695,0.00005953474,0.00002384877,0.001140468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002958043,"about_ca_system_score_gemma":0.0000047575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001666856,"about_ca_topic_score_gemma":0.000001585216,"domain_scores_codex":[0.9997486,0.000005520756,0.00003407004,0.00008029354,0.00005377362,0.00007775593],"domain_scores_gemma":[0.9997687,0.000007426364,0.000005534155,0.000167929,0.00002110239,0.00002933927],"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":[7.23802e-8,0.0000347879,0.000427326,7.355521e-7,0.000002396375,0.000001179555,0.0000287164,9.876255e-7,0.0006456636,0.08739889,0.02035266,0.8911066],"study_design_scores_gemma":[0.0004281639,0.0002972887,0.02697446,0.00001113185,0.000002633184,0.00003539062,0.00005695116,0.8484262,0.001109261,0.02024537,0.1019676,0.0004454814],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1944095,0.00007402773,0.4522757,0.006194333,0.000473448,0.0003974973,6.25033e-8,0.002292272,0.3438832],"genre_scores_gemma":[0.9746215,8.65128e-7,0.01936904,0.0006033244,0.00001579084,0.00002662988,1.089605e-7,0.000001022224,0.005361693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8906611,"threshold_uncertainty_score":0.9996372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009612997847025665,"score_gpt":0.2108643998043617,"score_spread":0.201251401957336,"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."}}