{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005399822,0.0009424399,0.001104094,0.001487862,0.0007473386,0.0008108639,0.001856854,0.001050576,0.002647932],"category_scores_gemma":[0.002560099,0.0003204152,0.0006011061,0.00175465,0.0004713866,0.001511265,0.001334648,0.001269657,0.002271604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007334415,"about_ca_system_score_gemma":0.001191279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005410727,"about_ca_topic_score_gemma":0.004811741,"domain_scores_codex":[0.9989715,0.0001256976,0.00006881614,0.0002274734,0.0005072181,0.00009934902],"domain_scores_gemma":[0.9992192,0.0001420361,0.00006215662,0.0001725615,0.0003669853,0.00003707233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005057736,0.0001351294,0.001525982,0.0001350613,0.00007052557,0.000173654,0.0001943665,0.1022763,0.02817456,0.008882646,0.008528603,0.8493974],"study_design_scores_gemma":[0.00006704511,0.0001199122,0.0006180754,0.00001605762,0.00002490799,0.0003724396,0.00005698359,0.9713586,0.01355584,0.003638381,0.01013315,0.00003854706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009450611,0.0004493916,0.9866744,0.0001156812,0.0001268625,0.00007155208,0.0000748195,0.001846087,0.001190594],"genre_scores_gemma":[0.2304153,0.0004852749,0.7638296,0.0001390096,0.0001082716,0.0002030616,0.000537343,0.0001358582,0.004146274],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005410727,"threshold_uncertainty_score":0.01075846,"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."}}