{"id":"W2142137929","doi":"10.1109/wcnc.2011.5779231","title":"Support Vector Machines for indoor sensor localization","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Wireless sensor network; Support vector machine; Real-time computing; Node (physics); Position (finance); Indoor positioning system; Embedded system; Computer network; Artificial intelligence; Accelerometer; Engineering; Operating system","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":[0.000838553,0.0009578422,0.001128706,0.0007527489,0.0002811833,0.0008175583,0.0008895861,0.0009260001,0.003720167],"category_scores_gemma":[0.003854554,0.0002589854,0.0004696109,0.001507104,0.0003002154,0.0009009002,0.00056125,0.001451899,0.001768888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003696323,"about_ca_system_score_gemma":0.0004897486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002779587,"about_ca_topic_score_gemma":0.001733567,"domain_scores_codex":[0.9991568,0.0003031544,0.0000678583,0.000157879,0.0002421669,0.00007210422],"domain_scores_gemma":[0.9986387,0.0007729254,0.0001156544,0.0001109539,0.0003305713,0.00003112999],"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":[0.0001703499,0.0000903633,0.00105554,0.0003561729,0.0001170527,0.0001214586,0.00006042287,0.3004039,0.003053912,0.0155512,0.0108407,0.668179],"study_design_scores_gemma":[0.00001092107,0.0000407937,0.000510092,0.00002297128,0.00000935901,0.00003557509,0.00001890352,0.9837281,0.0008950715,0.01035068,0.004362723,0.00001493912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005068079,0.003276395,0.9875493,0.0003899031,0.0002108029,0.00004900584,0.0002814172,0.001533956,0.001641255],"genre_scores_gemma":[0.4652534,0.004887633,0.5154086,0.0002188108,0.0006479258,0.0005621809,0.002101123,0.0002029317,0.01071741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003720167,"threshold_uncertainty_score":0.01244521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02131941551345031,"score_gpt":0.2164018481681559,"score_spread":0.1950824326547056,"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."}}