{"id":"W333771347","doi":"10.5281/zenodo.42819","title":"A Network Shadow Fading Model For Autonomous Infrastructure Wireless Networks","year":2012,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Fading; Computer science; Base station; Computer network; Channel (broadcasting); Wireless network; Shadow mapping; Shadow (psychology); Wireless; Stochastic geometry models of wireless networks; Software deployment; Radio resource management; Telecommunications; Artificial intelligence","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009555222,0.0001728051,0.0001713973,0.0001114416,0.003061686,0.0009157748,0.00217165,0.00008441085,0.000466411],"category_scores_gemma":[0.0001449925,0.0001812385,0.0000652269,0.0006688595,0.00007780817,0.000733474,0.002212911,0.0003205141,0.0002366797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001666491,"about_ca_system_score_gemma":0.000007043196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.78933e-7,"about_ca_topic_score_gemma":2.912686e-7,"domain_scores_codex":[0.9982476,0.0002634436,0.0002651654,0.0003538891,0.0002028877,0.0006669859],"domain_scores_gemma":[0.9983776,0.00005967032,0.0001259471,0.0007693464,0.0004295063,0.0002379344],"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":[0.00002578435,0.00007932311,0.00001676919,0.00002254137,0.00003876911,8.326065e-7,0.003310838,0.1164381,0.0003482149,0.2353546,0.2052393,0.4391249],"study_design_scores_gemma":[0.0002300734,0.00003851125,0.0001464379,0.00001897441,0.000005099516,0.0000222042,0.0000232331,0.701102,0.0000147149,0.0006199267,0.2976171,0.0001617079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009305289,0.0004071218,0.9844542,0.0006542082,0.0002079543,0.0004469442,0.00001457299,0.0008562143,0.01202832],"genre_scores_gemma":[0.9837644,0.0001949305,0.01375894,0.0006383951,0.0004339653,3.024764e-7,0.0002692267,0.0005133241,0.0004265681],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9828338,"threshold_uncertainty_score":0.9982362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04923122209233672,"score_gpt":0.2584303691091832,"score_spread":0.2091991470168465,"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."}}