{"id":"W3120984923","doi":"10.5539/nct.v5n2p40","title":"Indoor Localization Based on Fingerprint Clustering","year":2020,"lang":"en","type":"article","venue":"Network and Communication Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"RSS; Computer science; Cluster analysis; Fingerprint (computing); Gaussian; Fingerprint recognition; Data mining; Artificial intelligence; SIGNAL (programming language); Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003448341,0.0008363433,0.0009330466,0.002121963,0.0006090817,0.0007723236,0.001004176,0.0007333726,0.001806589],"category_scores_gemma":[0.001242518,0.0003030248,0.0006606716,0.002843979,0.0003485788,0.001159466,0.0009504656,0.0003924406,0.001473056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000503647,"about_ca_system_score_gemma":0.0005892417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00600526,"about_ca_topic_score_gemma":0.003348486,"domain_scores_codex":[0.9989491,0.0001688546,0.00004511483,0.000300735,0.0004039289,0.0001322776],"domain_scores_gemma":[0.9994819,0.00007528973,0.00007394384,0.0001146784,0.0002282369,0.00002590574],"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":[0.0004462854,0.0001067222,0.006424959,0.0003075706,0.0001165717,0.000290262,0.00021424,0.1479628,0.04964404,0.004960242,0.006673468,0.7828528],"study_design_scores_gemma":[0.00004413252,0.0002012428,0.006685924,0.00005333323,0.0001033008,0.001114567,0.0001701396,0.9403563,0.03706976,0.003787402,0.01029068,0.0001232116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02015062,0.0004964363,0.9722891,0.0000895952,0.0000889005,0.00005613321,0.000176132,0.0029654,0.00368767],"genre_scores_gemma":[0.7188113,0.001166727,0.2732834,0.0001049227,0.0001176306,0.0001393688,0.0008130445,0.0001625229,0.005401099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00600526,"threshold_uncertainty_score":0.0119406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270437953085269,"score_gpt":0.2002165457740009,"score_spread":0.1875121662431482,"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."}}