{"id":"W1881529840","doi":"10.1109/cscwd.2015.7230981","title":"Indoor location based on WiFi","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Position (finance); Scheme (mathematics); Track (disk drive); Set (abstract data type); Real-time computing; Calibration; Location data; Computer network; Statistics; Mathematics","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":[0.00005579026,0.00006043807,0.00004970156,0.0001163121,0.00001335242,0.00001420166,0.00007093125,0.00006141036,0.00003724611],"category_scores_gemma":[0.00005657891,0.00005208196,0.00001091702,0.0002707916,0.00001373095,0.00004631701,0.000005084774,0.00005213211,0.0002796352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004730493,"about_ca_system_score_gemma":0.00001455531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003342878,"about_ca_topic_score_gemma":0.000002718349,"domain_scores_codex":[0.9996755,0.000004427469,0.00007345981,0.00005994726,0.00009651414,0.00009012652],"domain_scores_gemma":[0.9997587,0.00001127122,0.000005900694,0.0001446911,0.0000479693,0.00003148698],"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":[0.00001179328,0.0000311786,0.001772149,0.00003286596,0.000008215539,0.000002870808,0.000114419,0.8850026,0.000264011,0.03328095,0.05882393,0.02065497],"study_design_scores_gemma":[0.0004946896,0.00006126338,0.0005301143,0.0000123265,0.000003253613,6.844928e-7,0.0001553145,0.8994704,0.07273408,0.0008115749,0.0255628,0.0001635076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0189562,0.00005592828,0.7186735,0.0002897878,0.0004830579,0.0001483916,0.000001714136,0.003461716,0.2579298],"genre_scores_gemma":[0.9981621,0.000001434116,0.001147337,0.0002082454,0.00002363412,0.000009702188,0.000007471813,0.00001194866,0.0004281354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9792059,"threshold_uncertainty_score":0.3594238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889674160591472,"score_gpt":0.211493634915475,"score_spread":0.1925968933095603,"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."}}