{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005611505,0.0004588212,0.0004184812,0.001206436,0.0001622462,0.0005720603,0.0003509506,0.0004849094,0.0003580266],"category_scores_gemma":[0.005121299,0.0003854011,0.000246773,0.001116065,0.0002370697,0.0007539467,0.0006390276,0.0004150498,0.0005534809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002585587,"about_ca_system_score_gemma":0.0002953316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00293461,"about_ca_topic_score_gemma":0.003901312,"domain_scores_codex":[0.9996649,0.0001170962,0.00001991098,0.00006511658,0.0001043567,0.00002861074],"domain_scores_gemma":[0.998168,0.0007008492,0.0003647822,0.0003028038,0.0004026201,0.0000608551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004494943,0.0001386565,0.1669945,0.0002964006,0.0002019422,0.0002998484,0.0003203574,0.5082253,0.04231287,0.002031922,0.001963212,0.2767656],"study_design_scores_gemma":[0.00001805149,0.0001504005,0.03249619,0.00002220732,0.00004948112,0.0002461064,0.0001013816,0.9542487,0.01020747,0.001494838,0.0009395031,0.00002573655],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4957781,0.0001915983,0.5019737,0.0000684343,0.00001508144,0.00003146902,0.0003628926,0.0008459447,0.000732797],"genre_scores_gemma":[0.9389194,0.0001861927,0.0597361,0.00001478427,0.00001049858,0.0000347064,0.0006406675,0.00002030131,0.0004373971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00293461,"threshold_uncertainty_score":0.005835056,"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."}}