{"id":"W2796932148","doi":"10.1109/lwc.2018.2859965","title":"Approximation of Meta Distribution and its Moments for Poisson Cellular Networks","year":2018,"lang":"en","type":"preprint","venue":"IEEE Wireless Communications Letters","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Poisson distribution; Distribution (mathematics); Applied mathematics; Mathematics; Scaling; Compound Poisson distribution; Zero-inflated model; Simple (philosophy); Statistical physics; Mathematical optimization; Computer science; Poisson regression; Mathematical analysis; Statistics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002854941,0.0002900378,0.0004973434,0.0001103118,0.0001367766,0.00004246684,0.0006516274,0.0002570479,0.00000195606],"category_scores_gemma":[0.00002148864,0.000329805,0.0001360014,0.0001530329,0.0001023183,0.0001742147,0.0002517831,0.0003097445,0.000001568802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001691211,"about_ca_system_score_gemma":0.00001207329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001314843,"about_ca_topic_score_gemma":0.000007115493,"domain_scores_codex":[0.9987134,0.0001085253,0.0005851462,0.0002615696,0.0001196705,0.0002116644],"domain_scores_gemma":[0.9978212,0.00014284,0.0003513432,0.001422846,0.0002104625,0.00005133353],"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":[0.00000841604,0.00003723319,0.00001733622,0.0007485286,0.000659144,9.844232e-8,0.0002302444,0.9751277,0.02100249,0.0005344152,0.001063017,0.0005713606],"study_design_scores_gemma":[0.0002428081,0.00001038507,0.00001693853,0.0001826438,0.0004524579,8.218427e-7,0.00001731037,0.9851134,0.01324276,0.0001180737,0.0003282133,0.0002741212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05240007,0.00253664,0.9422932,0.0003651332,0.0004575164,0.001334942,0.0003891292,0.0001956336,0.00002778615],"genre_scores_gemma":[0.9800012,0.001100308,0.01473693,0.00002612166,0.00009789095,0.0009326878,0.003020927,0.0000726185,0.0000113877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.927601,"threshold_uncertainty_score":0.9999154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03418746670374578,"score_gpt":0.2584592174538994,"score_spread":0.2242717507501537,"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."}}