{"id":"W2512240773","doi":"10.1109/tmc.2016.2604260","title":"How to Download More Data from Neighbors? A Metric for D2D Data Offloading Opportunity","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer network; Mobile device; Metric (unit); Object (grammar); Download; Mobile computing; Distributed computing; Operating system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008332361,0.0003623513,0.0004662663,0.0003447659,0.0005580849,0.00051309,0.004476048,0.0001512681,0.00004548586],"category_scores_gemma":[0.00003138257,0.0002911004,0.0001101948,0.0009080708,0.00006816625,0.001415733,0.0001847726,0.0002789452,0.00006015394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009543371,"about_ca_system_score_gemma":0.0002425943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008104598,"about_ca_topic_score_gemma":0.00002482486,"domain_scores_codex":[0.9966169,0.0001107315,0.0005096231,0.001609691,0.0004713953,0.0006816711],"domain_scores_gemma":[0.9927946,0.001897863,0.000212954,0.004355477,0.0001617992,0.0005773509],"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":[0.00003544897,0.0002037732,0.00001478699,0.00001652539,0.00009952774,0.00002240204,0.0001559972,0.001705666,0.0005434485,0.00005020294,0.01299998,0.9841523],"study_design_scores_gemma":[0.0008326256,0.0002054575,0.00001236398,0.0001580696,0.00007622968,0.00001758869,0.0000853424,0.9754565,0.0006602544,0.0001463218,0.02186468,0.0004846153],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002636759,0.00008338077,0.9892347,0.001888414,0.001971269,0.0007929406,0.002898372,0.0003992941,0.00009486203],"genre_scores_gemma":[0.9178651,0.00003964558,0.08041787,0.0007993299,0.0003426864,0.00004344045,0.000213252,0.00002904858,0.0002496258],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9836676,"threshold_uncertainty_score":0.9999541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.107448154472602,"score_gpt":0.3154034379914188,"score_spread":0.2079552835188168,"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."}}