{"id":"W2950764461","doi":"10.48550/arxiv.0810.4366","title":"Resource Allocation and Relay Selection for Collaborative Communications","year":2008,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Relay; Selection (genetic algorithm); Resource allocation; Computer science; Resource (disambiguation); Operations research; Telecommunications; Computer network; Artificial intelligence; Engineering","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":[0.00367097,0.001197758,0.001322415,0.0007228702,0.0009019244,0.001559444,0.001747317,0.001788453,0.003012527],"category_scores_gemma":[0.01256885,0.0005153765,0.0007628738,0.001264054,0.001973824,0.002397089,0.001597624,0.001104996,0.0005770227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001797616,"about_ca_system_score_gemma":0.00103141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00188001,"about_ca_topic_score_gemma":0.001275634,"domain_scores_codex":[0.9972632,0.001584218,0.00006478233,0.0003468958,0.000407915,0.0003329877],"domain_scores_gemma":[0.9915338,0.007013354,0.0005432765,0.0003970733,0.0003431421,0.0001694433],"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":[0.000165207,0.00006820407,0.0003415512,0.000118166,0.00005010125,0.0002249401,0.0001293721,0.8493043,0.002176871,0.130033,0.001666322,0.01572199],"study_design_scores_gemma":[0.00002985186,0.00005801311,0.00009025955,0.000009400705,0.00001449134,0.00008212271,0.00003020769,0.9428482,0.0007616736,0.05500604,0.001056808,0.00001280822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02190322,0.0005135138,0.9698607,0.0004470997,0.0000445288,0.00005225921,0.00004952023,0.0000754698,0.007053777],"genre_scores_gemma":[0.8521582,0.0008188788,0.1401956,0.0001613279,0.0001302059,0.0002597266,0.00007524113,0.00006155908,0.006139225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00367097,"threshold_uncertainty_score":0.01941419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1101327530641643,"score_gpt":0.3194014786671567,"score_spread":0.2092687256029924,"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."}}