{"id":"W2922887640","doi":"10.1109/jiot.2019.2906489","title":"An Adaptive Sampling Scheme via Approximate Volume Sampling for Fingerprint-Based Indoor Localization","year":2019,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Fingerprint (computing); Sampling (signal processing); Adaptive sampling; Scheme (mathematics); Fingerprint recognition; RSS; Wireless; Data mining; Algorithm; Real-time computing; Artificial intelligence; Computer vision; Mathematics; Statistics; Telecommunications; Monte Carlo method","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004593016,0.0002352606,0.0003375947,0.0003244685,0.00007336232,0.0001298073,0.0004716838,0.0002202596,0.00009208426],"category_scores_gemma":[0.00008309618,0.0002334024,0.0001566872,0.0001713655,0.00005451855,0.0005741917,0.00002568934,0.0004127912,0.00001854213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001606193,"about_ca_system_score_gemma":0.0000365052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001924611,"about_ca_topic_score_gemma":0.000001744861,"domain_scores_codex":[0.9985505,0.00002238696,0.0005998616,0.0002165082,0.0002569373,0.0003537569],"domain_scores_gemma":[0.999036,0.00007599328,0.0002687359,0.0002363003,0.0003018788,0.00008112183],"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":[0.0002966453,0.00009045869,0.005744518,0.0004390376,0.0001905894,0.000003101911,0.00161998,0.8724487,0.101934,0.001007919,0.0004358873,0.01578921],"study_design_scores_gemma":[0.0005906857,0.0002433103,0.00005255576,0.0002587021,0.00001903705,0.00001998795,0.000195041,0.8359793,0.1607794,0.001067775,0.0005625509,0.0002316754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1923176,0.00007217652,0.8060688,0.00002033941,0.0008882828,0.0002344757,0.000006130066,0.0003015609,0.00009053272],"genre_scores_gemma":[0.9055367,0.000009108168,0.09411321,0.0001019538,0.0001081069,0.00001232781,0.00001328825,0.00006517038,0.00004017861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.713219,"threshold_uncertainty_score":0.9517875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02095677382069503,"score_gpt":0.2548552377898855,"score_spread":0.2338984639691905,"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."}}