{"id":"W2775998236","doi":"10.1109/twc.2017.2785250","title":"Multi-Hop Cooperative Caching in Social IoT Using Matching Theory","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Army Research Office; National Natural Science Foundation of China; Beijing Nova Program; National Science Foundation","keywords":"Computer science; Internet of Things; Wireless; Scheme (mathematics); Computer network; Coding (social sciences); Matching (statistics); Distributed computing; Computer security; 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005519549,0.0001862398,0.0002284658,0.0002192542,0.004559455,0.0004902348,0.002927878,0.00009774799,0.000005357088],"category_scores_gemma":[0.00001058239,0.000199914,0.0001299894,0.0001818281,0.0002191599,0.0006566768,0.00003964064,0.000794541,0.00002144608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001688084,"about_ca_system_score_gemma":0.00009995111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00120679,"about_ca_topic_score_gemma":0.001774725,"domain_scores_codex":[0.9984287,0.0004979632,0.0003059586,0.0003183161,0.0001799761,0.0002690676],"domain_scores_gemma":[0.9969685,0.000321409,0.0001694467,0.002381427,0.00009678665,0.00006241951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001499039,0.004313839,0.0004964296,0.00005998323,0.0004473178,0.00004971425,0.05967901,0.3116138,0.1782692,0.1355903,0.00004853841,0.309282],"study_design_scores_gemma":[0.001032453,0.00002725926,0.001091582,0.0001667393,0.00002803918,0.00001521783,0.0007954331,0.9936895,0.001716209,0.0009660638,0.00005664836,0.0004148719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1995001,0.00004758813,0.7984772,0.001069276,0.0002672687,0.0001582756,0.00001490639,0.0001230895,0.000342245],"genre_scores_gemma":[0.9865664,0.00007570707,0.01288332,0.0001856082,0.00002374708,0.00004550295,0.000001614362,0.00001963671,0.0001984439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7870663,"threshold_uncertainty_score":0.9967365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0811028264004305,"score_gpt":0.3296931984993503,"score_spread":0.2485903720989198,"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."}}