{"id":"W3108914020","doi":"10.1109/access.2020.3039271","title":"A Survey of Machine Learning for Indoor Positioning","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":258,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Scalability; Adaptability; Non-line-of-sight propagation; Software deployment; Wireless; Machine learning; Artificial intelligence; Telecommunications","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":[0.001676673,0.001425425,0.00162536,0.002219497,0.0005122374,0.001659785,0.001888196,0.001706995,0.004082058],"category_scores_gemma":[0.005206742,0.0005976565,0.001191958,0.004957225,0.0006307677,0.002867011,0.001096584,0.002087201,0.003696004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007871606,"about_ca_system_score_gemma":0.001063782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002741731,"about_ca_topic_score_gemma":0.001950934,"domain_scores_codex":[0.9985375,0.0004395522,0.0001551339,0.000323552,0.000469901,0.00007432001],"domain_scores_gemma":[0.9976503,0.001471221,0.0000967989,0.000231998,0.0005058688,0.0000437393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004739136,0.0000762135,0.001700078,0.001830427,0.000119493,0.0001052567,0.00008192282,0.03228913,0.0008811271,0.02204247,0.02049582,0.9203307],"study_design_scores_gemma":[0.00002978587,0.0003557339,0.004751046,0.001874789,0.0001665112,0.0009292378,0.0002109949,0.426796,0.004116796,0.09203297,0.468574,0.0001622534],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.003865316,0.2783974,0.6943922,0.003046528,0.00140903,0.00009142273,0.000665546,0.001081245,0.01705135],"genre_scores_gemma":[0.1209779,0.497856,0.355238,0.0019814,0.005760241,0.0004242532,0.003292151,0.0003958978,0.01407417],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004082058,"threshold_uncertainty_score":0.01365584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04411765217212168,"score_gpt":0.2787286757262707,"score_spread":0.234611023554149,"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."}}