{"id":"W2893062876","doi":"10.5194/isprs-archives-xlii-1-379-2018","title":"EVALUATION OF DYNAMIC AD-HOC UWB INDOOR POSITIONING SYSTEM","year":2018,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Firmware; Node (physics); Computer science; Positioning system; Transceiver; Ultra-wideband; Multilateration; Indoor positioning system; Wireless ad hoc network; Real-time computing; Dynamic positioning; Range (aeronautics); Wireless; Engineering; Computer hardware; Telecommunications; Aerospace engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006413339,0.000456257,0.0004464681,0.0006127794,0.0002832289,0.0004021288,0.0004443903,0.000290465,0.0012156],"category_scores_gemma":[0.001403109,0.00007273979,0.0001458154,0.0004123859,0.0001956353,0.0004188961,0.0003260193,0.0001450342,0.000311042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000554137,"about_ca_system_score_gemma":0.0003005023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001971604,"about_ca_topic_score_gemma":0.001308493,"domain_scores_codex":[0.9992983,0.0002089763,0.00004034508,0.00009821667,0.0002825738,0.00007156689],"domain_scores_gemma":[0.999017,0.0002914785,0.00007124239,0.0001023885,0.0004607097,0.00005711857],"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":[0.003098072,0.0005665976,0.02468154,0.001165885,0.0003039086,0.0009268273,0.0002094568,0.6301354,0.1040994,0.001767454,0.002717126,0.2303284],"study_design_scores_gemma":[0.0001467172,0.007202812,0.0213107,0.00004136334,0.0002110431,0.0007740622,0.0003618796,0.8886174,0.07643731,0.0003998844,0.004445648,0.00005122793],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544592,0.00107335,0.03428294,0.00008844021,0.0001536829,0.0001455373,0.000245328,0.0007588468,0.008792629],"genre_scores_gemma":[0.9962321,0.0001250366,0.002724488,0.00001136355,0.000006273286,0.00001764611,0.0001493531,0.000007601303,0.0007260979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001971604,"threshold_uncertainty_score":0.004066586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01477802167380704,"score_gpt":0.2535475844525786,"score_spread":0.2387695627787716,"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."}}