{"id":"W2144723957","doi":"10.1109/tmc.2007.1017","title":"Kernel-Based Positioning in Wireless Local Area Networks","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":428,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Computer science; Hybrid positioning system; Wi-Fi; Local area network; Context (archaeology); Signal strength; Kernel (algebra); Location-based service; Wireless; Computer network; Wireless network; Histogram; Point (geometry); Location awareness; Ubiquitous computing; Wireless lan; Real-time computing; Positioning system; Telecommunications; Artificial intelligence; Geography; World Wide Web; Human–computer interaction","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":[0.0002258695,0.0001779119,0.0001744081,0.0003093061,0.0001561089,0.00003292063,0.0001364467,0.000183519,0.00002475252],"category_scores_gemma":[0.000001420783,0.0002028205,0.00007374308,0.0005802877,0.00006447786,0.00006595327,0.000001110883,0.0004544824,0.00001212445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002113449,"about_ca_system_score_gemma":0.00001248849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001897664,"about_ca_topic_score_gemma":0.0000752593,"domain_scores_codex":[0.9989307,0.00001694072,0.0003330677,0.000197739,0.0001268431,0.0003947173],"domain_scores_gemma":[0.9995358,0.0001740636,0.00002860047,0.0001820588,0.00002895938,0.0000505344],"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":[0.0000106124,0.00003793475,0.0001332224,0.00001654619,0.000008215058,0.00001532238,0.00006756084,0.8971224,0.0002710889,0.00004231575,0.00001066582,0.1022641],"study_design_scores_gemma":[0.0003892984,0.00004469633,0.0002108109,0.0001205356,0.000006716151,0.000006080251,0.0002123541,0.9343386,0.06441826,0.00001600958,0.00003552295,0.0002010716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1834049,0.00004577894,0.8145505,0.000006379765,0.0005125252,0.0001621801,0.000001951851,0.0009308183,0.0003850001],"genre_scores_gemma":[0.9987633,0.000009775722,0.001061441,0.00006046759,0.00003762225,0.00001574943,0.000004884414,0.00003839425,0.000008380427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8153583,"threshold_uncertainty_score":0.8270779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00660713434968295,"score_gpt":0.2152329872001624,"score_spread":0.2086258528504795,"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."}}