{"id":"W2952324612","doi":"10.48550/arxiv.1506.04729","title":"Optimal Forwarding in Opportunistic Delay Tolerant Networks with Meeting Rate Estimations","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Latency (audio); Computer network; Distributed computing; A priori and a posteriori; Node (physics); Network topology; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001154834,0.0005629117,0.00071509,0.0003983574,0.0002611881,0.0003208307,0.00167202,0.0004250897,0.00001632345],"category_scores_gemma":[0.00002229246,0.0005877717,0.0001665568,0.0009433408,0.0001711092,0.0005530688,0.001682387,0.001121787,0.00002250653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003625885,"about_ca_system_score_gemma":0.0009014844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001959899,"about_ca_topic_score_gemma":0.00009691665,"domain_scores_codex":[0.9968015,0.0002782753,0.000508435,0.00142162,0.0001826533,0.000807511],"domain_scores_gemma":[0.9970456,0.0003018107,0.000505554,0.001284242,0.000344121,0.0005186887],"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":[0.00006505812,0.00006898824,0.001467217,0.00003261152,0.00007187015,0.002907034,0.0001901069,0.9691077,4.837311e-7,0.02437873,0.0004125134,0.001297681],"study_design_scores_gemma":[0.0008320419,0.00008611332,0.0001112522,0.0004695052,0.0001178901,0.00005028997,0.0001102242,0.9924681,6.191197e-7,0.00489148,0.0001526202,0.0007098854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04892543,0.00009023178,0.9422929,0.00009984335,0.0005651613,0.0004443356,0.00001610488,0.0002841931,0.007281759],"genre_scores_gemma":[0.9778028,0.0000919239,0.02108526,0.0001228176,0.0001307677,0.000004897761,0.00009064861,0.00004068983,0.0006301935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9288774,"threshold_uncertainty_score":0.9996574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08570599335468317,"score_gpt":0.2013270093834637,"score_spread":0.1156210160287805,"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."}}