{"id":"W4388008106","doi":"10.1145/3616390.3618289","title":"New Machine Learning Hybrid Models to Lower Position Errors for Bluetooth-Based Indoor Localizations","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada); Sheridan College","funders":"","keywords":"Mean squared error; Computer science; Line (geometry); Artificial intelligence; Position (finance); Field (mathematics); Centroid; Point (geometry); Algorithm; Machine learning; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000775145,0.0008467186,0.0007817016,0.0007740046,0.0002790159,0.0007537216,0.001424623,0.0007245992,0.001153296],"category_scores_gemma":[0.002551371,0.0003207481,0.0006940548,0.000721349,0.0003667582,0.001004303,0.0006516833,0.0008037405,0.0006328365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004170433,"about_ca_system_score_gemma":0.000526952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005690379,"about_ca_topic_score_gemma":0.005688625,"domain_scores_codex":[0.9995876,0.00009634524,0.00002724123,0.00009990938,0.0001500587,0.00003889486],"domain_scores_gemma":[0.9991059,0.0003498527,0.0001084552,0.0000918951,0.0003247266,0.00001922185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007911456,0.0000600985,0.001509181,0.00005965584,0.00006282864,0.00005436256,0.00006873933,0.8831779,0.00312911,0.002425548,0.000767623,0.1086057],"study_design_scores_gemma":[0.000002320176,0.00001859779,0.0001937014,0.000003971184,0.000007770455,0.00001279653,0.000004332514,0.9985409,0.0004767632,0.0004333353,0.000300975,0.00000464312],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03539714,0.0005593315,0.9610456,0.0001439201,0.00008605115,0.0000326867,0.00007250159,0.0008378458,0.001824961],"genre_scores_gemma":[0.8643656,0.0005890912,0.1293233,0.0001600104,0.00009478868,0.0001458336,0.0002795256,0.0001317558,0.004910055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005690379,"threshold_uncertainty_score":0.01131451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01927212167692067,"score_gpt":0.2314197693856165,"score_spread":0.2121476477086958,"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."}}