{"id":"W2725906624","doi":"","title":"LC-KDE: A novel scheme for Wi-Fi localization","year":2016,"lang":"en","type":"article","venue":"Wireless Personal Multimedia Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Kernel density estimation; Computer science; Kernel (algebra); Fingerprint (computing); Artificial intelligence; Probability density function; Pattern recognition (psychology); Process (computing); Linear discriminant analysis; Selection (genetic algorithm); Kernel Fisher discriminant analysis; Scheme (mathematics); Multivariate statistics; Feature selection; Feature extraction; Discriminant; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001236785,0.0001944229,0.0001922655,0.0001437552,0.0002706409,0.00003189175,0.0007337634,0.0001927552,0.00006356496],"category_scores_gemma":[0.0001884992,0.0001619539,0.00009655634,0.0002870962,0.0002905531,0.0002115952,0.0001245515,0.0001350125,0.0000801462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001381788,"about_ca_system_score_gemma":0.00004205177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001232734,"about_ca_topic_score_gemma":0.00006177874,"domain_scores_codex":[0.9990321,0.00001900933,0.0002954214,0.0001880046,0.0001634037,0.0003020442],"domain_scores_gemma":[0.9984462,0.0004071415,0.00005425303,0.000812517,0.000203028,0.00007686969],"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":[0.0001051967,0.0008817854,0.009837911,0.0004757568,0.0005953177,0.000002003571,0.008333421,0.001774485,0.3309031,0.09479041,0.03472469,0.5175759],"study_design_scores_gemma":[0.001347855,0.0000246597,0.0003539156,0.0001182759,0.00002422505,0.000004272481,0.0004494307,0.9204615,0.01334393,0.0002869728,0.0632227,0.0003622052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00856526,0.0005047037,0.9855978,0.002007939,0.0002527155,0.0005114216,0.0003447025,0.001418846,0.0007966076],"genre_scores_gemma":[0.9559093,0.0006261778,0.04221977,0.0001073452,0.00007335748,0.0006229084,0.0001627233,0.00006344399,0.0002150146],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.947344,"threshold_uncertainty_score":0.6604287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03187044238233092,"score_gpt":0.2616010900842167,"score_spread":0.2297306477018858,"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."}}