{"id":"W2939699006","doi":"10.3390/s19071678","title":"Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Component (thermodynamics); Scale (ratio); Computer science; Environmental science; Geography; Physics; Cartography","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":[0.00005990794,0.0001865613,0.0001952254,0.0001642677,0.0000762791,0.00002971534,0.0001245475,0.0001667448,0.00008776985],"category_scores_gemma":[0.00001929641,0.0001849921,0.00004915556,0.0003035099,0.00004682657,0.00008129688,0.00003560021,0.0001801812,0.0007597626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001017771,"about_ca_system_score_gemma":0.00001009683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006412082,"about_ca_topic_score_gemma":0.000007970251,"domain_scores_codex":[0.9990044,0.00003356313,0.0002427298,0.0002171731,0.0002281052,0.0002740181],"domain_scores_gemma":[0.9994593,0.00004171472,0.000046245,0.0003139184,0.00008196614,0.0000568601],"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":[0.00002141597,0.00002950506,0.0146766,0.0001718981,0.00003705842,0.000004746852,0.001103787,0.9695413,0.005991783,0.005919402,0.001700777,0.0008016839],"study_design_scores_gemma":[0.0009873984,0.0000974377,0.008749435,0.00008853866,0.00001323372,0.000005028277,0.001008939,0.7651594,0.1433605,0.000617663,0.07948115,0.0004312983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881381,0.00004530439,0.007336802,0.00007973483,0.0005543104,0.0003893227,0.00002247114,0.001116574,0.002317314],"genre_scores_gemma":[0.9987159,0.00003211565,0.0004840049,0.0002764194,0.0000456411,0.00001068116,0.0001336613,0.00004415031,0.0002574125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.204382,"threshold_uncertainty_score":0.9765465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314858143798135,"score_gpt":0.2189088996991527,"score_spread":0.2057603182611714,"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."}}