{"id":"W4390597123","doi":"10.5383/juspn.17.02.001","title":"New and Reliable Points Shifting - Based Algorithm for Indoor Location Services","year":2022,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada); Sheridan College","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Algorithm; Set (abstract data type); Metric (unit); Multipath propagation; Grid; Upper and lower bounds; k-nearest neighbors algorithm; Bounded function; Data mining; Artificial intelligence; Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000433065,0.0001323781,0.0002738624,0.0001366393,0.0002440416,0.0001010136,0.0001310582,0.00009690662,0.000009288888],"category_scores_gemma":[0.00001194898,0.00012204,0.00004655881,0.0001940846,0.00001449939,0.0001499553,0.00004155432,0.0002469138,1.896147e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006945313,"about_ca_system_score_gemma":0.00004467024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000732992,"about_ca_topic_score_gemma":0.00001087833,"domain_scores_codex":[0.9990823,0.00003127454,0.0004078355,0.0001079799,0.0001713609,0.0001991971],"domain_scores_gemma":[0.9993452,0.0001080201,0.000214075,0.00008895689,0.0001668833,0.00007688365],"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":[0.00004753508,0.00001697359,0.002076281,0.0006134241,0.0001134273,0.00001907371,0.0008178127,0.8856159,0.00008028405,0.0002749796,0.00694732,0.103377],"study_design_scores_gemma":[0.0008939744,0.0002681705,0.0002251821,0.0001998257,0.00004437061,0.0001550802,0.002268769,0.980942,0.0001265948,0.0001090016,0.01461193,0.0001550844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03378827,0.02924491,0.9338391,0.000206163,0.002215932,0.0004755301,0.00001849299,0.0001358651,0.00007575405],"genre_scores_gemma":[0.9908937,0.0005163333,0.007552035,0.0001325929,0.0007624355,0.00002652329,0.000009357424,0.00003828992,0.00006876919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9571054,"threshold_uncertainty_score":0.4976649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006482775096025587,"score_gpt":0.2005911130684243,"score_spread":0.1941083379723987,"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."}}