{"id":"W2102839637","doi":"10.1109/vtcf.2006.489","title":"Wireless Indoor Positioning System with Enhanced Nearest Neighbors in Signal Space Algorithm","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Software deployment; k-nearest neighbors algorithm; Wireless; Algorithm; Signal strength; SIGNAL (programming language); Real-time computing; Indoor positioning system; System deployment; Wireless network; Artificial intelligence; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008477638,0.0003071357,0.0003664975,0.0006320399,0.0001110331,0.00006594999,0.0003888786,0.0005546749,0.00001259366],"category_scores_gemma":[0.000006253147,0.0002928217,0.00003791292,0.001174062,0.0002796556,0.0001531063,0.00003798449,0.0005397779,0.00003797386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001962544,"about_ca_system_score_gemma":0.00005805702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001410188,"about_ca_topic_score_gemma":0.0002258547,"domain_scores_codex":[0.9985539,0.00002500209,0.000336379,0.0003690168,0.0002070359,0.0005086488],"domain_scores_gemma":[0.9993486,0.00002661973,0.00007517084,0.0003689729,0.000147481,0.00003309247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000670499,0.0002076748,0.01332394,0.0005106277,0.0001877587,0.001439633,0.0002412855,0.2441833,0.397975,0.2566132,0.0002214706,0.08502903],"study_design_scores_gemma":[0.0008390917,0.000120674,0.0005623178,0.0003931971,0.00001806055,0.00008861806,0.0007241548,0.296572,0.6992564,0.0008171378,0.0001157995,0.0004925785],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4533983,0.0001187058,0.5428108,0.00009051642,0.0001030441,0.0002731069,0.000008026927,0.002214077,0.0009833866],"genre_scores_gemma":[0.9958065,0.00001622896,0.003831018,0.00001060483,0.00003504214,0.0002050888,0.00001828605,0.00004703148,0.00003024756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5424081,"threshold_uncertainty_score":0.9999524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003513089250181001,"score_gpt":0.1736049085118732,"score_spread":0.1700918192616922,"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."}}