{"id":"W2150082340","doi":"10.1109/spawc.2011.5990479","title":"Joint estimation of emitter power and location in cognitive radio networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Cognitive radio; Common emitter; Joint (building); Computer science; Shadow mapping; Power (physics); Joint probability distribution; Iterative method; Algorithm; Grid; Mathematical optimization; Telecommunications; Wireless; Mathematics; Artificial intelligence; Electronic engineering; Statistics; Engineering","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":[0.00004658592,0.00004665066,0.00006705097,0.00008045739,0.000006466817,0.00000323639,0.0000199508,0.00006150641,0.000076301],"category_scores_gemma":[0.00002999465,0.00004296912,0.000006314424,0.0001499521,0.0000293356,0.0000759302,0.000008425117,0.00004631692,0.000002585367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001052024,"about_ca_system_score_gemma":0.000002420027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001794988,"about_ca_topic_score_gemma":0.000008554422,"domain_scores_codex":[0.9997322,0.000004264768,0.0001209312,0.00005021822,0.00002903947,0.00006333077],"domain_scores_gemma":[0.9998878,0.00001058692,0.0000135138,0.0000457645,0.00003398097,0.000008340152],"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":[0.00009039784,0.0002027988,0.08502764,0.0004461512,0.0001599582,0.0000197617,0.01702778,0.5658358,0.00116255,0.04548572,0.003731472,0.28081],"study_design_scores_gemma":[0.0004141917,0.00005171561,0.08964245,0.0000866196,0.000008745874,0.000004208305,0.0007377744,0.8522205,0.05561812,0.00106553,0.000008121944,0.0001420488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.284422,0.0001277076,0.7073406,0.000006040537,0.00004725199,0.00008427622,4.024559e-7,0.0001386675,0.007833078],"genre_scores_gemma":[0.9985901,0.00002604672,0.001334086,0.00001429259,0.000002632952,0.000005992683,0.000003420947,0.00000598641,0.0000175185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.714168,"threshold_uncertainty_score":0.175223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519271665139744,"score_gpt":0.2051367855410302,"score_spread":0.1899440688896328,"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."}}