{"id":"W2594681962","doi":"10.1109/tim.2017.2666278","title":"High-Accuracy Localization Platform Using Asynchronous Time Difference of Arrival Technology","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Multilateration; Computer science; Asynchronous communication; Real-time computing; Time of arrival; Ranging; Non-line-of-sight propagation; Transmitter; Channel (broadcasting); Reliability (semiconductor); Baseband; Synchronization (alternating current); Electronic engineering; Computer hardware; Embedded system; Wireless; Telecommunications; Engineering; Bandwidth (computing)","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":[0.0004054899,0.0005539726,0.0004924526,0.0005776018,0.0002964903,0.000606501,0.00151773,0.0006803374,0.002689737],"category_scores_gemma":[0.0008183312,0.0002249455,0.000348241,0.0004345388,0.0002565672,0.0009014368,0.0009603561,0.0006279528,0.001465514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002947901,"about_ca_system_score_gemma":0.0006032877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008697551,"about_ca_topic_score_gemma":0.0007698914,"domain_scores_codex":[0.9995308,0.00006449504,0.00002160408,0.00009032737,0.0002419885,0.00005082417],"domain_scores_gemma":[0.9995623,0.00006240419,0.00006946774,0.0001004027,0.0001719384,0.00003347019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006055717,0.0002876328,0.005292888,0.0008181224,0.0001285281,0.0009903418,0.000495088,0.05438593,0.5597308,0.01967361,0.01037224,0.3472192],"study_design_scores_gemma":[0.0003087304,0.001882212,0.00439851,0.00009295112,0.0001515963,0.00191928,0.0001710672,0.5974657,0.3216777,0.004812616,0.06693504,0.0001846584],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03116622,0.0002205024,0.9593843,0.0001291885,0.0001840178,0.00009903557,0.0001263333,0.004852466,0.003837958],"genre_scores_gemma":[0.5669266,0.0003280322,0.4249166,0.0001397605,0.00007504807,0.0002609404,0.0004052947,0.0001226276,0.006825131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002689737,"threshold_uncertainty_score":0.008998096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03003393737673151,"score_gpt":0.2431480494648054,"score_spread":0.2131141120880739,"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."}}