{"id":"W2131923954","doi":"10.1109/iccw.2009.5207992","title":"A Scheme for Indoor Localization through RF Profiling","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Signal strength; Computer science; Profiling (computer programming); Communication source; Triangulation; Scheme (mathematics); Data mining; SIGNAL (programming language); Received signal strength indication; Artificial intelligence; Pattern recognition (psychology); Computer vision; Real-time computing; Algorithm; Wireless sensor network; Wireless; Computer network; Mathematics; Telecommunications","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":[0.00004704048,0.0001068745,0.0001073006,0.00005464776,0.00006393585,0.00002893915,0.00009972597,0.0001181897,0.00002967377],"category_scores_gemma":[0.00005314296,0.00009748442,0.00004007441,0.0002191037,0.00001576604,0.0001707829,0.000007286612,0.00005721226,0.00001916575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003713358,"about_ca_system_score_gemma":0.000008057698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001535828,"about_ca_topic_score_gemma":0.000001584786,"domain_scores_codex":[0.9994403,0.000002318771,0.0001678655,0.0001157647,0.00007558049,0.0001981835],"domain_scores_gemma":[0.999759,0.0000150555,0.00001495633,0.000137882,0.00005625022,0.00001689583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000045907,0.0001015767,0.00220568,0.0003210342,0.0000764835,0.000003953426,0.0007733961,0.1002084,0.03765563,0.7755185,0.03181424,0.0512752],"study_design_scores_gemma":[0.0005098237,0.00007168567,0.00005588496,0.00001774733,0.000007896874,0.00000218965,0.000208626,0.4043503,0.5581185,0.01705628,0.01935852,0.0002425588],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005355085,0.000133055,0.979111,0.0001452383,0.0001309766,0.0003287524,0.000003561096,0.002003932,0.01278837],"genre_scores_gemma":[0.9420348,0.00003749557,0.05716957,0.000401077,0.00006416153,0.00003691874,0.00003525918,0.00002059168,0.0002001658],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9366797,"threshold_uncertainty_score":0.3975299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01529235038024643,"score_gpt":0.2462469276515188,"score_spread":0.2309545772712724,"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."}}