{"id":"W4391543839","doi":"10.1177/02783649241230640","title":"UTIL: An ultra-wideband time-difference-of-arrival indoor localization dataset","year":2024,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Multilateration; Non-line-of-sight propagation; Computer science; Real-time computing; Ultra-wideband; Inertial measurement unit; Artificial intelligence; Testbed; Ground truth; Fuze; Computer vision; Simulation; Wireless; Acoustics; 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":[0.0005938168,0.002732918,0.001460951,0.001951915,0.0006338804,0.0008883469,0.003490788,0.001940421,0.006738026],"category_scores_gemma":[0.002097905,0.0004326624,0.001269193,0.003016108,0.0003893826,0.0008646783,0.00196483,0.001340221,0.01581639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007183819,"about_ca_system_score_gemma":0.001172117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0165239,"about_ca_topic_score_gemma":0.03893687,"domain_scores_codex":[0.9991824,0.000137219,0.00009248596,0.0002484221,0.0002201964,0.000119327],"domain_scores_gemma":[0.9991688,0.0001540345,0.00007360299,0.000264638,0.0002635031,0.00007549402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006700169,0.0004110403,0.009125605,0.003432052,0.0003157447,0.0006734346,0.0001901231,0.01360425,0.006876176,0.001191776,0.8928995,0.07061028],"study_design_scores_gemma":[0.0007692701,0.0005917973,0.03362294,0.0009637084,0.0002735111,0.001515231,0.0007468861,0.04692772,0.01250372,0.004612306,0.8970938,0.0003790746],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01232795,0.001122745,0.01071326,0.0003029303,0.0002871406,0.000153895,0.9583215,0.01339736,0.003373177],"genre_scores_gemma":[0.009044008,0.0002164906,0.006565938,0.0001079301,0.00001951494,0.0001924426,0.9828692,0.0002200553,0.0007644739],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0165239,"threshold_uncertainty_score":0.03285545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05567293960629447,"score_gpt":0.3563202104892119,"score_spread":0.3006472708829174,"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."}}