{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006519355,0.0005407378,0.0005900231,0.001135454,0.0009289149,0.001011335,0.001824447,0.0009431443,0.003340149],"category_scores_gemma":[0.0018559,0.0003762672,0.0005050751,0.001472961,0.000582637,0.001552885,0.002365853,0.001052777,0.003896286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003302009,"about_ca_system_score_gemma":0.000519645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009060161,"about_ca_topic_score_gemma":0.001118399,"domain_scores_codex":[0.999064,0.0001999085,0.00006096378,0.000203409,0.0003701106,0.0001015631],"domain_scores_gemma":[0.9989219,0.000102451,0.0001100211,0.0006437684,0.0001541372,0.00006784189],"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":[0.0004599446,0.0002253108,0.002663465,0.0002153806,0.00008395502,0.0003250053,0.0005802573,0.02520151,0.1081004,0.08618718,0.01417843,0.7617791],"study_design_scores_gemma":[0.000149495,0.001212442,0.004399925,0.0001188471,0.0001828889,0.003332857,0.0002612317,0.4571897,0.1565947,0.04264117,0.3336361,0.0002805283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003959758,0.000131714,0.9908667,0.0001070087,0.00007471995,0.00008274184,0.0001092955,0.001833695,0.002834476],"genre_scores_gemma":[0.2284204,0.0004402952,0.7591144,0.0002357241,0.0001295834,0.000308135,0.000597021,0.0001348902,0.01061963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003340149,"threshold_uncertainty_score":0.0111739,"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."}}