{"id":"W2893834508","doi":"10.1109/mnet.2018.1700297","title":"Joint Opportunistic Routing and Intra-Flow Network Coding in Multi-Hop Wireless Networks: A Survey","year":2018,"lang":"en","type":"article","venue":"IEEE Network","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Computer network; Wireless network; Routing protocol; Linear network coding; Wireless; Wireless Routing Protocol; Dynamic Source Routing; Distributed computing; Lossy compression; Multiple description coding; Routing (electronic design automation); Telecommunications; Network packet; Artificial intelligence","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":[0.003516553,0.0003737837,0.0005453978,0.0001010014,0.0007753684,0.0004268183,0.001048748,0.0001744057,0.00001556479],"category_scores_gemma":[0.0001532967,0.0003845541,0.00006735194,0.001686802,0.0002586586,0.0003896125,0.0007444486,0.0006016909,0.00002355774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001155131,"about_ca_system_score_gemma":0.0001126247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006804879,"about_ca_topic_score_gemma":0.003306004,"domain_scores_codex":[0.9960932,0.001054638,0.0007639318,0.0007619648,0.0002447793,0.001081448],"domain_scores_gemma":[0.9973454,0.0008066606,0.0002807255,0.001028604,0.0002589604,0.0002796201],"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":[0.000239755,0.0004089205,0.1280216,0.00004956485,0.0002286837,0.0001805797,0.004659173,0.2304176,0.0003194451,0.04145383,0.08528707,0.5087337],"study_design_scores_gemma":[0.0006103156,0.00007775403,0.04998315,0.0003716752,0.000006881653,0.00001753457,0.00001302874,0.9468341,0.000009391049,0.0002644904,0.001369337,0.0004422686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01919744,0.001130365,0.9727736,0.0003418582,0.004963775,0.0004616898,0.000002661386,0.0002927348,0.000835932],"genre_scores_gemma":[0.9779992,0.001710632,0.01581423,0.001176194,0.003098194,0.00003458202,0.00001659237,0.00003881005,0.0001115815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9588017,"threshold_uncertainty_score":0.9998606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024289442613606,"score_gpt":0.2995819962318702,"score_spread":0.1971530519705095,"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."}}