{"id":"W2020723047","doi":"10.1109/glocomw.2013.6855653","title":"TLDTCA: A distributed approach to meeting heterogenous connectivity requirements to sink in M2M networks","year":2013,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Network topology; Distributed computing; Disjoint sets; Topology (electrical circuits); Topology control; Sink (geography); Computer network; Logical topology; Power consumption; Power (physics); Mathematics; Wireless network; Wireless; Key distribution in wireless sensor networks","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":[0.0006162857,0.0002153968,0.0002737125,0.0001464291,0.0001850045,0.0003864236,0.0008922883,0.00008125487,0.000003172027],"category_scores_gemma":[0.0000938457,0.0001998074,0.00005768179,0.0009288634,0.00001368348,0.0004280879,0.0009910489,0.0001713538,0.0001777106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001642375,"about_ca_system_score_gemma":0.0000300545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007757558,"about_ca_topic_score_gemma":0.00003070206,"domain_scores_codex":[0.997786,0.0001162055,0.0003628953,0.0006630503,0.0002542193,0.0008175919],"domain_scores_gemma":[0.9988838,0.0001123246,0.00006295605,0.0005540861,0.0001066104,0.0002802275],"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":[0.00007225084,0.002618837,0.1188883,0.000145678,0.0001940615,0.00008967549,0.01610109,0.1948484,0.006160306,0.006200651,0.1261332,0.5285476],"study_design_scores_gemma":[0.0004427124,0.0001590866,0.02299736,0.00006117181,0.000003199017,0.00001355022,0.00005882833,0.9725893,0.000866581,0.0004356095,0.001823041,0.000549608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3160445,0.00001370097,0.6767047,0.0004629332,0.001239916,0.0005229455,1.455483e-7,0.0001927711,0.004818422],"genre_scores_gemma":[0.9359748,3.642411e-7,0.06228524,0.001148077,0.0004568179,0.00006140612,0.000005075168,0.00001281904,0.00005534041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7777408,"threshold_uncertainty_score":0.8147909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02769700188163237,"score_gpt":0.2422904997583296,"score_spread":0.2145934978766972,"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."}}