{"id":"W2006741433","doi":"10.1007/s11227-014-1195-9","title":"GAER: genetic algorithm-based energy-efficient routing protocol for infrastructure-less opportunistic networks","year":2014,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer network; Routing protocol; Hop (telecommunications); Latency (audio); Zone Routing Protocol; Distance-vector routing protocol; Distributed computing; Routing (electronic design automation); Wireless Routing Protocol; Telecommunications","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":[0.0005773384,0.0005238521,0.0005513745,0.0006556476,0.0006413045,0.0006971124,0.001341445,0.000840269,0.00145879],"category_scores_gemma":[0.001416913,0.0001832217,0.0003604978,0.0007720598,0.0004774554,0.0006891057,0.0008845545,0.0008316142,0.0003011671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006625534,"about_ca_system_score_gemma":0.001306008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003965994,"about_ca_topic_score_gemma":0.006775781,"domain_scores_codex":[0.9997125,0.0000722494,0.00001169252,0.00004547448,0.00009949064,0.00005854032],"domain_scores_gemma":[0.9995679,0.0001761162,0.0000621801,0.00007283302,0.00009100031,0.00002984225],"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":[0.0002460114,0.0001503215,0.0009423741,0.0001105045,0.0001050932,0.0002963525,0.0001618925,0.7798952,0.01537319,0.03803626,0.008299285,0.1563835],"study_design_scores_gemma":[0.00004009543,0.00008335974,0.000235298,0.00000943309,0.00002390592,0.000117098,0.00003141409,0.9816477,0.003822461,0.009534546,0.004435276,0.00001940363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04836561,0.0004350166,0.9399036,0.0004478972,0.0002078144,0.0001979389,0.0002529758,0.003328716,0.006860517],"genre_scores_gemma":[0.691829,0.0004328911,0.2971267,0.0003119922,0.00003894816,0.0002770381,0.0004754732,0.0002156913,0.009292315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003965994,"threshold_uncertainty_score":0.007885814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02180578335023805,"score_gpt":0.2538559763131712,"score_spread":0.2320501929629331,"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."}}