{"id":"W2914482263","doi":"10.1109/hpcc/smartcity/dss.2018.00095","title":"Fuzzy Soft-Set Based Approach for Femto-Caching in Wireless Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Wireless network; Wireless; Computer network; Fuzzy logic; Set (abstract data type); Fuzzy set; Femto-; Distributed computing; Artificial intelligence; Programming language; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005672274,0.0001342027,0.0001646686,0.0001113002,0.0001559138,0.000178652,0.0006969706,0.00007688576,0.000003296724],"category_scores_gemma":[0.00001705874,0.000118085,0.00008493666,0.0002714021,0.00003948405,0.0002669341,0.00009929833,0.0001484681,0.000008844531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003792396,"about_ca_system_score_gemma":0.00004022683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002014366,"about_ca_topic_score_gemma":0.00008669498,"domain_scores_codex":[0.9987928,0.00006093699,0.0001980087,0.0004288747,0.0001495538,0.0003697836],"domain_scores_gemma":[0.9992365,0.0001381649,0.0000486174,0.0004429281,0.00006177289,0.0000719896],"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":[0.0004367986,0.001160156,0.02679822,0.000182126,0.0001174731,0.00003609451,0.002734497,0.1434347,0.002968455,0.2685984,0.03230987,0.5212231],"study_design_scores_gemma":[0.0005775482,0.00007984846,0.0003128073,0.0000158745,0.000002775298,0.000002624776,0.00003001894,0.997993,0.00008232312,0.0004628176,0.0002646821,0.0001756724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02911717,0.0000274129,0.9639468,0.0002422105,0.0003075457,0.0002059087,0.00000134467,0.0001933763,0.005958194],"genre_scores_gemma":[0.9720844,0.000001259853,0.02594972,0.001347963,0.0002241139,0.00003687339,0.000009955324,0.00001061843,0.0003351226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9429672,"threshold_uncertainty_score":0.4815365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03008026499044329,"score_gpt":0.2454048585669545,"score_spread":0.2153245935765112,"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."}}