{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005012409,0.0001135638,0.0001097812,0.00009265011,0.0000474375,0.0000132119,0.0001039776,0.000107037,0.0006251867],"category_scores_gemma":[0.00003996943,0.0000984806,0.00004444222,0.0001383693,0.00002695346,0.00009334037,0.00001380544,0.00004411559,0.00007792657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002077199,"about_ca_system_score_gemma":0.000007035203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000122152,"about_ca_topic_score_gemma":0.00001565391,"domain_scores_codex":[0.999476,0.000004044558,0.0001663192,0.000108345,0.00006196427,0.0001833976],"domain_scores_gemma":[0.9997354,0.00001609822,0.00001564061,0.0001481923,0.00005495483,0.00002971286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002828563,0.000393095,0.07067135,0.001754988,0.0005844872,0.00004315889,0.007767419,0.01818537,0.01966894,0.5013263,0.2265818,0.1527403],"study_design_scores_gemma":[0.001121637,0.0002052391,0.002646806,0.0000155459,0.00004629717,0.00001520717,0.0004384458,0.3429241,0.5931491,0.006251503,0.05248889,0.000697247],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006719032,0.0000289698,0.9589547,0.00002665374,0.0004420136,0.0002996888,0.00001925718,0.002211576,0.03129811],"genre_scores_gemma":[0.9892514,0.000014341,0.009578347,0.0001204608,0.00005266829,0.00005448277,0.00003886869,0.00003960029,0.000849864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9825323,"threshold_uncertainty_score":0.6845356,"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."}}