{"id":"W3112893815","doi":"10.3390/s20247003","title":"Indoor Positioning System Using Dynamic Model Estimation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Samsung; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universidade Federal do Amazonas","keywords":"Computer science; Computation; Node (physics); Position (finance); SIGNAL (programming language); Bluetooth; RSS; Real-time computing; Set (abstract data type); Scale (ratio); Simulation; Algorithm; Wireless; Engineering; 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":[0.0003122241,0.001034604,0.001013226,0.0009294057,0.0003789922,0.0007400559,0.0009522653,0.000712014,0.001911022],"category_scores_gemma":[0.0014925,0.000409195,0.0006597875,0.001334339,0.0002198544,0.001197069,0.001125454,0.0007746639,0.001973055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003482105,"about_ca_system_score_gemma":0.0005417345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003819654,"about_ca_topic_score_gemma":0.00391332,"domain_scores_codex":[0.9994509,0.0001004528,0.00002443478,0.0001717632,0.0002100172,0.00004240602],"domain_scores_gemma":[0.9996243,0.00007174852,0.00005250947,0.0001131689,0.0001234954,0.00001483546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001508388,0.00008492301,0.002959402,0.0001800799,0.0001628144,0.0001550541,0.00008750035,0.4529163,0.02741134,0.006447083,0.005519231,0.5039254],"study_design_scores_gemma":[0.00001820086,0.00006606968,0.001007969,0.00001290573,0.00002811425,0.0001822477,0.0000208929,0.9849228,0.005810714,0.002869224,0.005032042,0.00002890755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004148407,0.0001115385,0.9925225,0.00003569239,0.0000394716,0.00001543388,0.00009901116,0.001892945,0.001134933],"genre_scores_gemma":[0.4434044,0.0005087943,0.549281,0.0001202234,0.00009334303,0.000136025,0.001459294,0.0002225601,0.004774314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003819654,"threshold_uncertainty_score":0.007594824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376588556201055,"score_gpt":0.2144878743723445,"score_spread":0.200721988810334,"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."}}