{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001851624,0.001472486,0.001765849,0.002177958,0.0005318538,0.001767339,0.002055294,0.001860973,0.00400888],"category_scores_gemma":[0.005834883,0.0006645439,0.001314129,0.005153339,0.0006962512,0.003090331,0.001212465,0.002234326,0.004027959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008671349,"about_ca_system_score_gemma":0.001064867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003080523,"about_ca_topic_score_gemma":0.001963102,"domain_scores_codex":[0.9983023,0.0005183207,0.0001723211,0.0004047323,0.0005189746,0.00008330029],"domain_scores_gemma":[0.9973015,0.001671906,0.0001073951,0.0002989076,0.0005692304,0.00005110542],"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":[0.00004920198,0.00007444192,0.001497485,0.001799385,0.0001252868,0.0000991635,0.00008427294,0.03322629,0.0008958073,0.02337684,0.02218209,0.9165897],"study_design_scores_gemma":[0.00002813232,0.000329955,0.004555226,0.001688505,0.0001537856,0.0008240946,0.0002115364,0.4309076,0.004276637,0.09832342,0.458544,0.0001569253],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.003522285,0.2624139,0.7128476,0.003081481,0.001436314,0.00008022101,0.0006667428,0.001071826,0.01487963],"genre_scores_gemma":[0.11472,0.470154,0.3875167,0.002152704,0.006209548,0.0004164334,0.00357197,0.0004770343,0.01478153],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.00400888,"threshold_uncertainty_score":0.01341105,"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."}}