{"id":"W3003275070","doi":"10.1109/icpads47876.2019.00137","title":"An Adaptive Probability Prediction Routing Scheme in Urban DTNs","year":2019,"lang":"en","type":"article","venue":"","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Node (physics); Predictability; Computer network; Routing protocol; Latency (audio); Routing (electronic design automation); Overhead (engineering); Geographic routing; Dynamic Source Routing; Zone Routing Protocol; Scheme (mathematics); Distributed computing; Mathematics; Engineering","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":[0.0006528141,0.0004952928,0.0005686545,0.000599749,0.0008706129,0.0005099052,0.001273109,0.0004883941,0.0006768097],"category_scores_gemma":[0.001698536,0.0002326174,0.0003389861,0.0008452781,0.000531127,0.001137471,0.001061399,0.0005358986,0.0001686399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007487469,"about_ca_system_score_gemma":0.0008256421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005522787,"about_ca_topic_score_gemma":0.005870142,"domain_scores_codex":[0.9995921,0.0001021255,0.0000251165,0.00009861626,0.0001211125,0.00006093013],"domain_scores_gemma":[0.9994994,0.0001894121,0.00008609108,0.00007419605,0.0001120106,0.0000387696],"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":[0.0001711212,0.00004564187,0.001474648,0.0001140375,0.00004490363,0.0002677857,0.0002270428,0.8084876,0.008866518,0.03003309,0.004452021,0.1458156],"study_design_scores_gemma":[0.000009150462,0.00006154515,0.0002346953,0.000006625194,0.00001363007,0.0001159454,0.00003658101,0.9905571,0.0008819846,0.006090112,0.001975196,0.00001745182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04463094,0.0008833379,0.9497594,0.0004426084,0.0001724283,0.0001084158,0.0001294925,0.0006333476,0.003240013],"genre_scores_gemma":[0.9334406,0.0006480782,0.06342106,0.0001200584,0.00007676762,0.00009483715,0.0001714219,0.00002583886,0.002001336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005522787,"threshold_uncertainty_score":0.01098126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02378600007173991,"score_gpt":0.2306100133917115,"score_spread":0.2068240133199716,"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."}}