{"id":"W3009794420","doi":"10.23919/cnsm46954.2019.9012721","title":"Lumped Markovian Estimation for Wi-Fi Channel Utilization Prediction","year":2019,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Channel (broadcasting); Markov process; Representation (politics); State (computer science); Enhanced Data Rates for GSM Evolution; Matrix (chemical analysis); Process (computing); Edge device; Stochastic matrix; Wireless network; Task (project management); Wireless; Algorithm; Control theory (sociology); Markov chain; Computer network; Mathematics; Machine learning; Artificial intelligence; Engineering; Telecommunications; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001146651,0.0006582857,0.0009810793,0.0006779679,0.0004752992,0.0009101467,0.001605404,0.000833376,0.002130492],"category_scores_gemma":[0.00528668,0.0006656214,0.0005822287,0.0008780361,0.0007373921,0.002041432,0.0008948398,0.001701446,0.0004206286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144434,"about_ca_system_score_gemma":0.000906276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01697505,"about_ca_topic_score_gemma":0.0143762,"domain_scores_codex":[0.9995267,0.0001365868,0.0000268804,0.0001396289,0.00009109407,0.00007898623],"domain_scores_gemma":[0.9976062,0.001651197,0.0002340939,0.0002386791,0.0002054733,0.00006440371],"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":[0.00004414745,0.0000273264,0.0009823699,0.00001173212,0.00002036208,0.00002147391,0.00002287233,0.9868246,0.0004027232,0.00287237,0.0001723628,0.008597597],"study_design_scores_gemma":[0.000001087526,0.000004024175,0.000089488,0.000001200012,0.000001856388,0.000002318169,0.000001700618,0.9980322,0.0001076463,0.001723461,0.00003256129,0.000002457647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04140396,0.0001276564,0.9564748,0.0001589489,0.0000252348,0.00003134472,0.0001988744,0.0008791795,0.0007000331],"genre_scores_gemma":[0.9398711,0.0001774537,0.05728734,0.00007318157,0.00003752501,0.000106373,0.0003700346,0.00004988323,0.002027074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01697505,"threshold_uncertainty_score":0.0337525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011492280423054,"score_gpt":0.2739588475531511,"score_spread":0.2438439247489206,"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."}}