{"id":"W4320029327","doi":"10.1109/globecom48099.2022.10001198","title":"Community-Oriented Resource Allocation at the Extreme Edge","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Job shop scheduling; Bipartite graph; Resource allocation; Resource management (computing); Enhanced Data Rates for GSM Evolution; Distributed computing; Exploit; Context (archaeology); Edge computing; Computer network; Graph; Computer security; Theoretical computer science; Routing (electronic design automation); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001243967,0.0005143971,0.0006774828,0.0005110783,0.001190407,0.001346814,0.001795482,0.000758627,0.003057806],"category_scores_gemma":[0.002126922,0.0002663879,0.0004697713,0.0006251817,0.0007826755,0.001993509,0.002211994,0.0008417508,0.0004261128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000990598,"about_ca_system_score_gemma":0.001580804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00356531,"about_ca_topic_score_gemma":0.004700312,"domain_scores_codex":[0.9990999,0.0002871301,0.00002484874,0.0001733699,0.0001409494,0.0002739177],"domain_scores_gemma":[0.9989055,0.000405346,0.00009587323,0.0001921614,0.0001877513,0.0002133363],"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":[0.0005875224,0.0003422118,0.001277612,0.0002159271,0.00008983658,0.000332222,0.0003347381,0.7688464,0.01762381,0.09664442,0.009529188,0.1041761],"study_design_scores_gemma":[0.00003411489,0.00006194536,0.0002491667,0.00001168125,0.00001316907,0.00008106874,0.00009960277,0.9596629,0.002016431,0.03423901,0.003515342,0.00001566612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1160365,0.0007284204,0.8693258,0.0006827321,0.0001194384,0.0002006648,0.0001425886,0.0008478716,0.01191598],"genre_scores_gemma":[0.8660058,0.0001968775,0.1306124,0.0001787193,0.00002543627,0.0001005583,0.0001042738,0.0001001927,0.002675783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00356531,"threshold_uncertainty_score":0.01022941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07502582090834353,"score_gpt":0.2864199305602017,"score_spread":0.2113941096518582,"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."}}