{"id":"W1988435521","doi":"10.1109/issnip.2011.6146596","title":"Multi-target device-free tracking using radio frequency tomography","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Beijing University of Posts and Telecommunications","keywords":"Testbed; RSS; Tracking (education); Computer science; Metric (unit); Wireless sensor network; Attenuation; Real-time computing; Radio frequency; Tracking error; Artificial intelligence; SIGNAL (programming language); Computer vision; Algorithm; Computer network; Engineering; 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":[0.0007281132,0.0005076801,0.000526723,0.0004199228,0.0003186721,0.0007879561,0.0007398031,0.001045666,0.0003256969],"category_scores_gemma":[0.002812773,0.0002426353,0.000376366,0.0004925609,0.0005858327,0.001623673,0.0008718694,0.0003198299,0.0001611369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003734425,"about_ca_system_score_gemma":0.0003790522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198876,"about_ca_topic_score_gemma":0.001219527,"domain_scores_codex":[0.9994022,0.0001821407,0.00002293614,0.0001281343,0.0002150497,0.000049563],"domain_scores_gemma":[0.9988996,0.0005530281,0.0001678116,0.0002284986,0.0001141669,0.00003694797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000587045,0.0001896618,0.01358427,0.0002879207,0.0001600629,0.0005659365,0.0004215318,0.5957194,0.1928803,0.005743229,0.0005454042,0.1893153],"study_design_scores_gemma":[0.00003491501,0.000346164,0.006143779,0.0000199158,0.00004715862,0.0007563218,0.00006466643,0.9100925,0.07908191,0.001744874,0.001621217,0.00004654613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2499517,0.0004727894,0.7467316,0.0001005821,0.00003884602,0.00005091014,0.00003006286,0.0006461311,0.001977249],"genre_scores_gemma":[0.8872849,0.0002383604,0.1114619,0.0000438502,0.00000903622,0.00003539636,0.00006198997,0.0000297489,0.0008349016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001198876,"threshold_uncertainty_score":0.003850698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04826724765405522,"score_gpt":0.2313672907319994,"score_spread":0.1831000430779442,"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."}}