{"id":"W4306252504","doi":"10.1088/1361-6501/ac9a64","title":"Position estimation and calibration for high precision human positioning and tracking using millimeter-wave radar","year":2022,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Computer science; Radar; Extremely high frequency; Position (finance); Calibration; Tracking (education); Context (archaeology); Precise Point Positioning; Positioning system; Millimeter; Remote sensing; Computer vision; Point (geometry); Global Positioning System; Telecommunications; Optics; Mathematics; Physics; Geology","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":[0.0007460578,0.00008919149,0.00009803558,0.0005049969,0.001096202,0.00008610887,0.00006995419,0.0000612962,0.000002563089],"category_scores_gemma":[0.0001028828,0.00009498479,0.000006808833,0.0005064156,0.0002169811,0.0003283596,0.000079121,0.00009398441,4.829914e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002177742,"about_ca_system_score_gemma":0.00002405741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009455595,"about_ca_topic_score_gemma":0.000006232211,"domain_scores_codex":[0.9990739,0.00001108884,0.0001548454,0.0002232646,0.0003662844,0.0001705699],"domain_scores_gemma":[0.9996706,0.0000112998,0.00004186398,0.00009440467,0.0001611591,0.0000206751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005464285,0.00001246016,0.0004328167,0.00003958325,0.000007749624,0.000001248437,0.0001426937,0.002551566,0.9068779,0.01379519,0.0000210679,0.07611223],"study_design_scores_gemma":[0.0005058157,0.0002796856,0.0009914234,0.00004194671,0.00003073866,0.0000728779,0.0004928128,0.4944184,0.478773,0.02413098,0.00005706408,0.0002052779],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7513753,0.0003254731,0.247369,0.0002696884,0.00008073459,0.0002833378,0.000005176662,0.0002742629,0.0000169638],"genre_scores_gemma":[0.9907898,0.00001319463,0.009112732,0.0000174795,0.000007292009,0.00004320656,0.000006262428,0.00000914095,9.095748e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4918668,"threshold_uncertainty_score":0.843121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03533224261007113,"score_gpt":0.242629484195089,"score_spread":0.2072972415850179,"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."}}