{"id":"W2266339649","doi":"10.1145/2856636.2856654","title":"Experiences with using iBeacons for Indoor Positioning","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Beacon; Bluetooth Low Energy; Bluetooth; Computer science; Broadcasting (networking); Protocol (science); Embedded system; Real-time computing; Computer network; Wireless; Telecommunications","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":[0.00002311313,0.00006450527,0.00006194307,0.00005222761,0.00006631283,0.00001872415,0.00006032901,0.00004040208,0.00007281622],"category_scores_gemma":[0.00001263645,0.0000363835,0.00001615386,0.00007975609,0.00004783444,0.0001315979,0.000007208317,0.00001614015,0.000004194322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002621455,"about_ca_system_score_gemma":0.00000776336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002443547,"about_ca_topic_score_gemma":0.000004196336,"domain_scores_codex":[0.9996659,0.000001864169,0.00007416796,0.00007459014,0.00004611407,0.0001373192],"domain_scores_gemma":[0.9998387,0.00003157735,0.000009396305,0.00007560273,0.00002804439,0.0000167161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002167524,0.0001128066,0.02001441,0.0003639582,0.0004922161,0.00002798597,0.02625357,0.02713566,0.5904145,0.1546226,0.0091334,0.1712121],"study_design_scores_gemma":[0.0007282665,0.00008761333,0.0000764931,0.00008612665,0.00001269904,0.00001750293,0.009621966,0.02404381,0.9632059,0.0004807287,0.00134607,0.0002928068],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3631953,0.00002210542,0.6349298,0.00004034685,0.00005953942,0.00007652822,0.000002016825,0.0004919969,0.00118241],"genre_scores_gemma":[0.982029,0.000002655343,0.01774555,0.0000239507,0.00002298016,0.0000529523,7.825033e-7,0.00001288725,0.0001092764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6188337,"threshold_uncertainty_score":0.1483676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01187156755547252,"score_gpt":0.2157088925860145,"score_spread":0.203837325030542,"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."}}