{"id":"W2167002404","doi":"10.1109/ccece.2008.4564906","title":"Channel modeling in wireless optical communications using Markov chains","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Markov chain; Wireless; Channel (broadcasting); Markov process; Markov model; Computer network; Optical wireless; Telecommunications; Mathematics; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001605393,0.0008696163,0.001122414,0.0009441418,0.0009671076,0.00151703,0.001233114,0.00128816,0.002838464],"category_scores_gemma":[0.00539218,0.0007328294,0.0008875402,0.001131095,0.00165215,0.002183479,0.001091946,0.001812097,0.0004779518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001709632,"about_ca_system_score_gemma":0.001707417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02482462,"about_ca_topic_score_gemma":0.0130298,"domain_scores_codex":[0.9990694,0.0003724282,0.00003092488,0.0001199007,0.0002070666,0.0002002687],"domain_scores_gemma":[0.9953135,0.003684588,0.0003482258,0.0001791637,0.0003635133,0.000110907],"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":[0.00002079632,0.000009353598,0.0003078316,0.00001027122,0.000007433459,0.00002741376,0.00002255853,0.9796073,0.0001656934,0.0183209,0.0001789377,0.001321419],"study_design_scores_gemma":[0.000002397232,0.000002618084,0.00002509219,0.00000230197,0.000001419117,0.000003417257,0.000003096615,0.9956442,0.00005258352,0.004190353,0.00006986889,0.000002641208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04579098,0.0004993388,0.9489674,0.0005190475,0.00007974288,0.00007231848,0.000260994,0.0003540849,0.003456015],"genre_scores_gemma":[0.9367643,0.001149034,0.05305711,0.0001990922,0.000128583,0.000291263,0.0004500436,0.0001143214,0.007846288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02482462,"threshold_uncertainty_score":0.04936028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04124799867225599,"score_gpt":0.2130476933849347,"score_spread":0.1717996947126787,"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."}}