{"id":"W2035653651","doi":"10.1016/j.pmcj.2012.07.007","title":"Predicting missing contacts in mobile social networks","year":2012,"lang":"en","type":"article","venue":"Pervasive and Mobile Computing","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Popularity; Mobile social network; Range (aeronautics); Routing (electronic design automation); Social contact; Wireless sensor network; Delay-tolerant networking; Computer network; Human–computer interaction; Routing protocol; Mobile computing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002008754,0.0008123147,0.00143721,0.003066666,0.001120834,0.001412238,0.002285652,0.002566173,0.001130084],"category_scores_gemma":[0.0163138,0.000715151,0.0006189458,0.002131945,0.0007906834,0.003306259,0.001265844,0.001486416,0.0005079429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009548472,"about_ca_system_score_gemma":0.0005067692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009640301,"about_ca_topic_score_gemma":0.01635251,"domain_scores_codex":[0.9985678,0.0003433652,0.0001310961,0.0003814359,0.0003667467,0.0002095281],"domain_scores_gemma":[0.9805281,0.01553135,0.001713686,0.0006058572,0.0008687124,0.0007523501],"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":[0.001460862,0.0008985773,0.3342764,0.0004728207,0.0003560639,0.002069703,0.001450114,0.5406432,0.002692312,0.01022819,0.007329548,0.09812218],"study_design_scores_gemma":[0.0000209684,0.00009705745,0.01282599,0.00001989118,0.00004445153,0.0003279534,0.0004016226,0.975492,0.0004944623,0.009643013,0.0006165209,0.00001601905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9335678,0.001253602,0.05987113,0.001333144,0.0001644226,0.0001258233,0.001528425,0.0002540071,0.00190171],"genre_scores_gemma":[0.9947048,0.0002343611,0.003542243,0.00004727242,0.0001048116,0.00002821282,0.0006130725,0.00001100071,0.0007141991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009640301,"threshold_uncertainty_score":0.01916838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02189167057009864,"score_gpt":0.2646715877179974,"score_spread":0.2427799171478988,"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."}}