{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006684476,0.0002087328,0.0003201932,0.00008107597,0.0004303489,0.0001787263,0.0002870549,0.0001325026,0.000007924653],"category_scores_gemma":[0.00001057252,0.0002089345,0.00006699637,0.000263814,0.00006075259,0.000474647,0.0004149073,0.0003299727,0.000004251371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004840135,"about_ca_system_score_gemma":0.00004946379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004046262,"about_ca_topic_score_gemma":0.000004731707,"domain_scores_codex":[0.9981977,0.000110483,0.0003821254,0.0003755938,0.0001806999,0.0007533753],"domain_scores_gemma":[0.9990482,0.0003567163,0.0001176144,0.0001952276,0.00006239332,0.000219883],"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":[0.00000966745,0.0001351124,0.1400488,0.00003907808,0.00002329388,0.00004512987,0.01179235,0.00227862,0.00009249029,0.001179751,0.0003244003,0.8440313],"study_design_scores_gemma":[0.0005036385,0.00007652674,0.01145475,0.0001105373,0.00001142272,0.00006679122,0.0007119086,0.9841191,0.00002864102,0.00008195984,0.0025416,0.0002931611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5456097,0.003514865,0.4485091,0.00004579486,0.0005840341,0.0002755988,8.924073e-7,0.0001167001,0.001343316],"genre_scores_gemma":[0.9955428,0.00008426401,0.002890189,0.0004313582,0.0009816495,0.00002468302,0.000005838087,0.00001599391,0.00002326001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9818404,"threshold_uncertainty_score":0.8520103,"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."}}