{"id":"W4387092561","doi":"10.1109/lcomm.2023.3320035","title":"Multi-Tag Localization in Cooperative AmBC","year":2023,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"Huawei Technologies","keywords":"Computer science; SIGNAL (programming language); Backscatter (email); Subspace topology; Multiple signal classification; Signal subspace; Radio frequency; Power (physics); Direction of arrival; Signal-to-noise ratio (imaging); Algorithm; Speech recognition; Telecommunications; Artificial intelligence; Wireless; Antenna (radio); Noise (video)","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":[0.0001154114,0.000107727,0.0001111734,0.0003402844,0.0001236509,0.00003195202,0.0005541071,0.00007609461,0.00001007743],"category_scores_gemma":[0.00004979321,0.0001186427,0.00002684619,0.0012391,0.0001266079,0.0001395757,0.00007214559,0.0001968385,0.0002812897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000947884,"about_ca_system_score_gemma":0.000008335323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002501645,"about_ca_topic_score_gemma":0.0002036096,"domain_scores_codex":[0.9993476,0.00005309137,0.0002231113,0.0001055623,0.00007880031,0.0001918844],"domain_scores_gemma":[0.9990255,0.00007902796,0.00002017974,0.0008178646,0.00003653218,0.00002092565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001671165,0.00003207771,0.002746294,0.00002411082,0.00002132374,0.000004860131,0.001237381,0.9361528,0.03130132,0.0009843814,0.02532208,0.002171678],"study_design_scores_gemma":[0.0003812271,0.00000564897,0.002153481,0.00003536588,0.000004846716,0.000001256338,0.0004363652,0.9677966,0.01156613,0.00003819603,0.01736808,0.0002127943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2492016,0.0007766434,0.7230014,0.01267814,0.001106579,0.00110154,0.00005694944,0.009039213,0.003037942],"genre_scores_gemma":[0.9964418,0.0006863315,0.001893263,0.0006675148,0.0000121709,0.0001169746,0.0001054452,0.00002970138,0.00004683544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7472401,"threshold_uncertainty_score":0.4838109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03590839213080798,"score_gpt":0.2728929388301238,"score_spread":0.2369845466993158,"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."}}