{"id":"W3119651306","doi":"10.1155/2021/6660990","title":"Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Fingerprint (computing); Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001767032,0.000194988,0.0002437834,0.0001381465,0.0006582006,0.0001387305,0.00035738,0.0001568443,0.00002451747],"category_scores_gemma":[0.00004163932,0.0002259649,0.00005847523,0.000558604,0.0001421374,0.0001237784,0.0004022286,0.0003793546,0.000006158465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008389205,"about_ca_system_score_gemma":0.00004184631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001618332,"about_ca_topic_score_gemma":0.00001749063,"domain_scores_codex":[0.9988777,0.0001129278,0.0003753523,0.000233826,0.0001236419,0.0002765608],"domain_scores_gemma":[0.9986763,0.0002022908,0.00008603017,0.0007871832,0.0001908695,0.00005734447],"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":[0.000001635871,0.00005113622,0.00705291,0.00009802578,0.00002897353,0.000003622584,0.0007598823,0.8614535,0.003310746,0.00102696,0.00001501683,0.1261976],"study_design_scores_gemma":[0.0002819363,0.00002120875,0.0001568249,0.000119044,0.00002089432,0.00001489859,0.001271256,0.9750404,0.009907716,0.00003470873,0.01287676,0.0002544011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4175827,0.00367006,0.5772984,0.00001402718,0.0001060861,0.0002128871,0.000003202568,0.0007682209,0.0003443986],"genre_scores_gemma":[0.9838374,0.001014448,0.01483508,0.00005936976,0.0000291538,0.00005450027,0.00009900783,0.00005121256,0.00001981661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5662547,"threshold_uncertainty_score":0.9214584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419628459963699,"score_gpt":0.2522507124189284,"score_spread":0.2380544278192915,"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."}}