{"id":"W3155584426","doi":"10.11591/eei.v10i3.2763","title":"Indoor positioning system based on magnetic fingerprinting-images","year":2021,"lang":"en","type":"article","venue":"Bulletin of Electrical Engineering and Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Università degli Studi di Padova","keywords":"Inertial measurement unit; Computer science; Global Positioning System; Dead reckoning; RSS; Computer vision; Real-time computing; Path (computing); Artificial intelligence; SIGNAL (programming language); 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":[0.0001526358,0.0005326868,0.0004405321,0.00126538,0.0002084677,0.0005779072,0.0004782077,0.0003706875,0.003941204],"category_scores_gemma":[0.000462921,0.0001297135,0.0002697465,0.001033249,0.0001102773,0.000550179,0.0004951771,0.0002371584,0.002558722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001660632,"about_ca_system_score_gemma":0.0001927973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001691027,"about_ca_topic_score_gemma":0.001674685,"domain_scores_codex":[0.9996294,0.00004738203,0.00002112081,0.00009426672,0.0001686796,0.00003910638],"domain_scores_gemma":[0.9998124,0.00001574804,0.00002793032,0.00004314993,0.0000897805,0.00001112037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006067022,0.0001066595,0.0110072,0.0005601235,0.00008717048,0.0003801388,0.0002000838,0.01415904,0.164216,0.002546158,0.01056463,0.795566],"study_design_scores_gemma":[0.0001652748,0.001458247,0.0921688,0.0002984344,0.0004599189,0.00507438,0.0007050754,0.4202242,0.375,0.004452013,0.0996425,0.0003513035],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1179107,0.001252877,0.8395889,0.0001741895,0.0003907093,0.0002136672,0.003420703,0.01504325,0.02200505],"genre_scores_gemma":[0.808446,0.0008226126,0.1755914,0.0001165607,0.0001328154,0.0002384406,0.002782263,0.0001830571,0.01168684],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003941204,"threshold_uncertainty_score":0.01318467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002191173891977016,"score_gpt":0.1518972565107338,"score_spread":0.1497060826187568,"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."}}