{"id":"W4308125763","doi":"10.32920/21476622","title":"A Survey of Machine Learning for Indoor Positioning","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Scalability; Non-line-of-sight propagation; Software deployment; Adaptability; Wireless; Machine learning; Artificial intelligence; Real-time computing; Telecommunications; Database; Software 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003468961,0.0001686576,0.0002984119,0.000208279,0.00008182355,0.00002382085,0.0002572928,0.0001942731,0.000407418],"category_scores_gemma":[0.0002327823,0.0001802977,0.00009320596,0.0001739431,0.00002321738,0.00002500406,0.0003324945,0.0005417048,0.000001888704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007413238,"about_ca_system_score_gemma":0.00002894655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004561722,"about_ca_topic_score_gemma":0.000119378,"domain_scores_codex":[0.9991772,0.00004245223,0.000311272,0.0001765176,0.0001267254,0.0001658175],"domain_scores_gemma":[0.9994302,0.0001582347,0.00007884953,0.0002157798,0.00009941367,0.00001752816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001857229,0.00001663685,0.02043968,0.0006362341,0.0001339505,0.000001072883,0.0001777133,0.9715802,0.0002358954,0.001299332,0.0008198147,0.004640881],"study_design_scores_gemma":[0.0004414307,0.00008610096,0.01031506,0.00006318977,0.00003747403,0.000001516329,0.0001524062,0.9710109,0.01385926,0.0009108731,0.002658212,0.0004635611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08199839,0.002506555,0.8981425,0.00005250585,0.001339641,0.001102278,0.0007699267,0.003331041,0.01075713],"genre_scores_gemma":[0.9949867,0.0001076163,0.00290574,0.00001188079,0.00001596399,0.0001469689,0.001393465,0.00005336926,0.0003782951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9129883,"threshold_uncertainty_score":0.7352325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225138844987278,"score_gpt":0.2524820965989718,"score_spread":0.229968212100244,"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."}}