{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004879147,0.0001020208,0.0001356761,0.00004648732,0.00004342269,0.00007341775,0.0003685024,0.0000793681,0.00005977754],"category_scores_gemma":[0.000003630452,0.00007909429,0.00003018103,0.0002517413,0.00002356814,0.0006668809,0.0001046268,0.0001660543,0.00005962099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006028828,"about_ca_system_score_gemma":0.00008861086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007458705,"about_ca_topic_score_gemma":0.00002490127,"domain_scores_codex":[0.9988102,0.00006742306,0.0002396397,0.0004325126,0.0001890058,0.0002612299],"domain_scores_gemma":[0.9992175,0.00004902218,0.00005560049,0.0005257379,0.00005336952,0.00009873074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006354952,0.0004732932,0.5953046,0.00001922522,0.00001675128,0.00002015154,0.002223685,0.001175443,0.0002951218,0.309324,0.001055007,0.09002914],"study_design_scores_gemma":[0.0002370567,0.000193457,0.0143991,0.00001737454,0.000001121179,0.000003511378,0.00006381623,0.9816378,0.00002319587,0.003158866,0.0001583101,0.0001064172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1815129,0.000007473016,0.7936982,0.0001101723,0.0002868564,0.0002844093,0.000001625792,0.0002039601,0.0238944],"genre_scores_gemma":[0.9589828,9.263582e-7,0.04027633,0.0001422943,0.00006715006,0.000008017329,0.000004231506,0.000004647372,0.0005136228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9804623,"threshold_uncertainty_score":0.3225372,"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."}}