{"id":"W4400314541","doi":"10.1109/jsen.2024.3420727","title":"MLA-MFL: A Smartphone Indoor Localization Method for Fusing Multisource Sensors Under Multiple Scene Conditions","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Engineering Laboratory","keywords":"Computer science; Computer vision; Artificial intelligence; Acoustics; Real-time computing; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003945676,0.0003151385,0.0003217833,0.0005814258,0.0004414295,0.0003257187,0.0001678224,0.0002968461,0.00009018309],"category_scores_gemma":[0.0001997843,0.0003032935,0.0002245025,0.0006239258,0.00008923366,0.0002807944,0.00001788351,0.0005401997,0.00006779211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002522071,"about_ca_system_score_gemma":0.00005266981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001277468,"about_ca_topic_score_gemma":0.00001866062,"domain_scores_codex":[0.9982523,0.00008990741,0.0005656948,0.0002838132,0.0002823871,0.0005259347],"domain_scores_gemma":[0.9989403,0.0004153375,0.00007741696,0.0002117634,0.0002123808,0.0001428392],"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":[0.00002016491,0.0000273583,0.0001566961,0.0001657457,0.0001938698,0.00005826058,0.0007763937,0.9546949,0.02827607,0.0005686565,0.008282488,0.00677945],"study_design_scores_gemma":[0.0008040378,0.00003902719,0.00008707168,0.0001629492,0.00009425815,0.000528256,0.0009916242,0.8808471,0.1040251,0.001310579,0.01075223,0.0003578021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08368042,0.0004915319,0.9110222,0.0002554851,0.002481145,0.0003303534,0.00009590901,0.001368025,0.0002748867],"genre_scores_gemma":[0.9648544,0.0001952428,0.03362965,0.0001363892,0.0006114103,0.00002335637,0.000042508,0.0001448258,0.0003622008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.881174,"threshold_uncertainty_score":0.9999419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183247197010876,"score_gpt":0.2828291637788562,"score_spread":0.2645044440777686,"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."}}