{"id":"W2004011015","doi":"10.1109/iscc.2014.6912513","title":"An efficient heuristic candidate selection algorithm for Opportunistic Routing in wireless multihop networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Selection algorithm; Network packet; Selection (genetic algorithm); Heuristic; Algorithm; Computer network; Node (physics); Wireless; Routing (electronic design automation); Reliability (semiconductor); Wireless network; Engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.001034282,0.0002051271,0.0002548199,0.000108857,0.000191489,0.0002053393,0.000622791,0.0001131504,0.000008512648],"category_scores_gemma":[0.0000421211,0.0001981304,0.00005049693,0.0004754087,0.00003126107,0.0001834589,0.0001106338,0.0002041226,0.00000605992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001201842,"about_ca_system_score_gemma":0.00006417235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003009428,"about_ca_topic_score_gemma":0.0003405903,"domain_scores_codex":[0.9979537,0.0001593556,0.0003993613,0.0006552983,0.0002001427,0.0006321124],"domain_scores_gemma":[0.9987319,0.0003286603,0.0001352749,0.0004973182,0.0001042099,0.000202617],"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":[0.000007576773,0.0001558142,0.0006563208,0.00001022417,0.000007857579,0.000006393062,0.0001195898,0.2280694,0.00005601989,0.07077519,0.0002026687,0.6999329],"study_design_scores_gemma":[0.0005805155,0.0001693509,0.0007528878,0.00002784457,0.000006244398,0.00001125475,0.000009885662,0.9977957,0.00005235575,0.0001995824,0.0001375559,0.0002568198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006524549,0.00000417777,0.9917807,0.00004078262,0.0005779837,0.0005053491,0.000001725469,0.000271808,0.0002929551],"genre_scores_gemma":[0.9082682,0.000002840123,0.09097999,0.0002147683,0.0002777329,0.0001055927,0.00002499241,0.00002210308,0.0001037724],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9017437,"threshold_uncertainty_score":0.8079522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01050922587154063,"score_gpt":0.2484601978332459,"score_spread":0.2379509719617052,"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."}}