{"id":"W2148844368","doi":"10.1109/glocom.2009.5425746","title":"An Adaptive Forwarding Scheme for Message Delivery over Delay Tolerant Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer network; Scheme (mathematics); Delay-tolerant networking; Power consumption; Network delay; Resource consumption; Distributed computing; Power (physics); Routing protocol; Routing (electronic design automation); Network packet","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":[0.0004562052,0.0002815571,0.0003432339,0.0000865376,0.0002899784,0.0002693353,0.0008287696,0.0001810013,0.00004649406],"category_scores_gemma":[0.000002911016,0.0002445742,0.0001829032,0.0002755115,0.00003607854,0.001042064,0.0000959629,0.0002001135,0.000009186704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004865136,"about_ca_system_score_gemma":0.00008454452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001732245,"about_ca_topic_score_gemma":0.000007452771,"domain_scores_codex":[0.9979472,0.00005023157,0.0003839062,0.0006478261,0.000288904,0.0006819323],"domain_scores_gemma":[0.9985485,0.0001713862,0.0001194614,0.0006897562,0.0001557806,0.0003151155],"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":[0.0002169046,0.0002432423,0.0001666637,0.000005630083,0.00007360114,0.00009224424,0.0002491578,0.0168092,0.00009485218,0.2930981,0.01907014,0.6698802],"study_design_scores_gemma":[0.0006587881,0.0005717506,0.0002278572,0.00002959705,0.00001775976,0.00002286336,0.00003505677,0.9933827,0.00004286832,0.002867288,0.001764673,0.0003788125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005033131,0.000363187,0.9855902,0.0002315176,0.0004098614,0.0003792042,0.000005212347,0.0003409406,0.00764672],"genre_scores_gemma":[0.8408251,0.00003228351,0.1556343,0.002829722,0.0003296304,0.0000128352,0.00001383887,0.0000144054,0.0003079468],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9765735,"threshold_uncertainty_score":0.9973446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084975737239216,"score_gpt":0.2593871057264299,"score_spread":0.2385373483540377,"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."}}