{"id":"W1998026332","doi":"10.1002/wcm.49","title":"Estimating position of mobile terminals from path loss measurements with survey data","year":2002,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Instituto de Telecomunicações","keywords":"Path loss; Computer science; Log-distance path loss model; Terminal (telecommunication); Position (finance); Radio propagation; Radio propagation model; Path (computing); Mobile telephony; Telecommunications; Probability density function; Mobile radio; Algorithm; Statistics; Computer network; Wireless; Mathematics","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":[0.0002847662,0.0001296459,0.0002145186,0.00006551524,0.0002072053,0.00004498981,0.0007366906,0.00006353667,0.00001082722],"category_scores_gemma":[0.00002240548,0.0001234888,0.00001417419,0.0002164208,0.0001390451,0.0001473719,0.0004273196,0.0001418323,0.000003119193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000236282,"about_ca_system_score_gemma":0.000006586858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00020309,"about_ca_topic_score_gemma":0.00006497844,"domain_scores_codex":[0.9991421,0.00007927683,0.0003249042,0.0001770969,0.0001294016,0.000147183],"domain_scores_gemma":[0.9981086,0.0002111456,0.0001109082,0.001429879,0.0001093398,0.00003015031],"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":[0.00001030087,0.000348627,0.1165844,0.000257399,0.0002264383,0.00000373498,0.004092537,0.109599,0.004155798,0.0001210553,0.0002971261,0.7643035],"study_design_scores_gemma":[0.0002497488,0.0000468101,0.003216459,0.0002310944,0.00002084228,0.000005400569,0.0002848185,0.9942313,0.001411862,0.00001638487,0.0001293773,0.0001558444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8926079,0.002754012,0.1035803,0.000008890567,0.0000521625,0.0002773392,0.0001517431,0.0002889345,0.0002786876],"genre_scores_gemma":[0.9725475,0.0003630527,0.02664789,0.000007788549,0.00001347198,0.00002645141,0.0003682534,0.00002226023,0.000003289477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8846323,"threshold_uncertainty_score":0.5035729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06426223181379154,"score_gpt":0.2760757523170608,"score_spread":0.2118135205032693,"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."}}