{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007382365,0.0004226597,0.0004440945,0.000754512,0.0008150339,0.0003933387,0.001294755,0.000624996,0.0008048441],"category_scores_gemma":[0.001700524,0.0001584438,0.0002933465,0.0004992918,0.0003921507,0.001253835,0.0006734286,0.0005350023,0.0001612014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005955637,"about_ca_system_score_gemma":0.0005005159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001264584,"about_ca_topic_score_gemma":0.001245496,"domain_scores_codex":[0.9996376,0.00006930337,0.00003676612,0.00007224329,0.0001449819,0.00003903013],"domain_scores_gemma":[0.9993548,0.0002390679,0.00007919663,0.0001151497,0.0001764937,0.00003531791],"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":[0.0006707724,0.0001819712,0.001866593,0.0004314084,0.0001373179,0.0006379732,0.0007791136,0.1786639,0.1687225,0.08046744,0.005023142,0.5624178],"study_design_scores_gemma":[0.0001075498,0.0004642611,0.000581713,0.00004200029,0.0001157943,0.0006957029,0.00008873105,0.9172904,0.03339014,0.02235221,0.02477371,0.00009774342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02639582,0.0006516499,0.9705508,0.000204144,0.0001988738,0.0001302843,0.00004950964,0.0005278561,0.001291166],"genre_scores_gemma":[0.653753,0.001009593,0.3413182,0.0002510085,0.0001671132,0.0002199242,0.0001711573,0.00004453418,0.003065459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001294755,"threshold_uncertainty_score":0.004321158,"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."}}