{"id":"W3094830184","doi":"10.3390/info11110504","title":"Energy-Efficient Check-and-Spray Geocast Routing Protocol for Opportunistic Networks","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Fundação para a Ciência e a Tecnologia; Ministério da Ciência, Tecnologia e Ensino Superior; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer network; Computer science; Geocast; Routing protocol; Network packet; Wireless Routing Protocol; Dynamic Source Routing; Distributed computing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003735518,0.0002872046,0.0003956925,0.0004348759,0.0004899187,0.0003735448,0.0006862106,0.0002790082,0.0003757574],"category_scores_gemma":[0.00092476,0.0001073415,0.0002483378,0.0004109052,0.0003379496,0.0005790725,0.000693981,0.0003860978,0.0000611227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004235612,"about_ca_system_score_gemma":0.001111064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002051306,"about_ca_topic_score_gemma":0.00432711,"domain_scores_codex":[0.9998447,0.00002864428,0.00001109684,0.0000170049,0.00007411868,0.00002442875],"domain_scores_gemma":[0.9996246,0.0001393383,0.00007898086,0.00004506648,0.00008622282,0.00002571345],"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":[0.0008243909,0.0002519059,0.004364893,0.0007121212,0.0002130962,0.001287489,0.0006788692,0.3104142,0.2128492,0.1067537,0.01277596,0.3488741],"study_design_scores_gemma":[0.0001429967,0.0005008861,0.00128637,0.00004515823,0.0001283666,0.000876406,0.0002139788,0.9166815,0.03898105,0.0181285,0.02294692,0.00006778136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1065794,0.001588754,0.8842267,0.0003820842,0.0002670464,0.0003493793,0.0001991712,0.0008619817,0.005545464],"genre_scores_gemma":[0.9140176,0.0009118417,0.08201631,0.0001481031,0.00003772868,0.0002180898,0.0002579677,0.00002838623,0.002364137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002051306,"threshold_uncertainty_score":0.004078686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04004653720944708,"score_gpt":0.2589569634157565,"score_spread":0.2189104262063095,"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."}}