{"id":"W2921537655","doi":"10.1049/iet-net.2018.5106","title":"Energy aware routing for efficient green communication in opportunistic networks","year":2019,"lang":"en","type":"article","venue":"IET Networks","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer network; Computer science; Routing protocol; Routing (electronic design automation); Overhead (engineering); Dynamic Source Routing; Link-state routing protocol; Static routing; Geographic routing; Node (physics); Distributed computing; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001002432,0.0003256559,0.0004710148,0.0001262621,0.0002204314,0.0002030354,0.001446852,0.0003207343,0.00002988516],"category_scores_gemma":[0.000004920132,0.0003300982,0.0001572495,0.0005993369,0.00006692911,0.0002145427,0.0005221434,0.0004212548,0.00001299873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001096116,"about_ca_system_score_gemma":0.0001198462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002075153,"about_ca_topic_score_gemma":0.0001022943,"domain_scores_codex":[0.9972683,0.0001843646,0.00069935,0.0006854646,0.0002876775,0.0008748577],"domain_scores_gemma":[0.9971381,0.0007499726,0.000318618,0.001424523,0.0001405095,0.0002282268],"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":[0.00006291078,0.0001497292,0.002403454,0.00002345214,0.00003590163,0.0000316904,0.0002165534,0.597941,0.000001709168,0.07293806,0.004628874,0.3215666],"study_design_scores_gemma":[0.0007970858,0.00009079571,0.0002356151,0.0001733943,0.00001370718,0.00001587662,0.00004178329,0.9929495,5.977751e-7,0.001234755,0.004052875,0.0003940254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001077144,0.0006576794,0.9925387,0.0004547886,0.001079686,0.0004974281,0.000005305483,0.0002005263,0.003488739],"genre_scores_gemma":[0.9934298,0.000166192,0.003727259,0.001135334,0.0003303941,0.00008964286,0.0001653362,0.00004126118,0.0009148254],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9923526,"threshold_uncertainty_score":0.9999151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01776157256606135,"score_gpt":0.2353543601491763,"score_spread":0.217592787583115,"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."}}