{"id":"W3197178084","doi":"10.32920/ryerson.14660685.v1","title":"Resource-Aware Topology Adaptation in P2P Overlay Adhoc Network","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Computer science; Overlay network; Computer network; Overlay; Mobile ad hoc network; Adaptation (eye); Wireless ad hoc network; Distributed computing; Software deployment; Network topology; Resource (disambiguation); Peer-to-peer; Abstraction; Wireless network; Topology (electrical circuits); Wireless; The Internet; Network packet; Telecommunications; Mathematics","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":[0.0005551072,0.0003473969,0.0005779989,0.0001317966,0.00009429996,0.0003071949,0.001143601,0.0006503045,0.0002948981],"category_scores_gemma":[0.00001062977,0.0003501246,0.0001647328,0.0004047024,0.00006483158,0.0001598604,0.002227296,0.0009869764,0.00002825665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001105189,"about_ca_system_score_gemma":0.0005460057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004075835,"about_ca_topic_score_gemma":0.0006840291,"domain_scores_codex":[0.9970236,0.0003118368,0.0006403374,0.001055513,0.0003580326,0.0006106185],"domain_scores_gemma":[0.9979324,0.000282504,0.000246143,0.001257604,0.0001137402,0.0001676242],"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":[0.00005680541,0.0002347084,0.002484255,0.0001430939,0.0001239561,0.001812753,0.003789442,0.2998931,0.000001608021,0.09071538,0.08794458,0.5128003],"study_design_scores_gemma":[0.0002447033,0.00003978719,0.0006977265,0.0001927188,0.00001160215,0.00003087254,0.0002829262,0.9797946,0.000001826592,0.008188256,0.01010272,0.0004122671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001358634,0.0008410666,0.9579071,0.001527242,0.002189167,0.0002681815,0.000003433106,0.0002332296,0.03567198],"genre_scores_gemma":[0.9377488,0.0002170909,0.05587829,0.00232732,0.0007745826,0.00005247385,0.0001929192,0.00002717832,0.002781389],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9363901,"threshold_uncertainty_score":0.9998951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03318987751051307,"score_gpt":0.252495818043315,"score_spread":0.2193059405328019,"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."}}