{"id":"W2142098273","doi":"10.1109/rose.2009.5355980","title":"A high precision sensor system for indoor object positioning and monitoring","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Object (grammar); Position (finance); Indoor positioning system; Measure (data warehouse); Real-time computing; Electro-optical sensor; Computer vision; Artificial intelligence; Engineering; Electronic engineering; Accelerometer; Data mining","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":[0.0006215136,0.0007043409,0.0007315713,0.001007727,0.000666654,0.0008180018,0.001464323,0.001364919,0.006036139],"category_scores_gemma":[0.0007629159,0.0004174419,0.0004441462,0.001149838,0.0004261642,0.001179588,0.0008742582,0.001076639,0.00353285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000311214,"about_ca_system_score_gemma":0.0005474681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007608848,"about_ca_topic_score_gemma":0.0009899772,"domain_scores_codex":[0.9986915,0.0001357385,0.0000503827,0.0002849052,0.0007464286,0.00009104104],"domain_scores_gemma":[0.9994313,0.0001051759,0.00006211885,0.0001520629,0.0002012551,0.00004811071],"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":[0.0004886971,0.0001718929,0.002484485,0.0005835348,0.0001160171,0.0003548359,0.0002211375,0.003877779,0.5718838,0.004474421,0.01224945,0.4030939],"study_design_scores_gemma":[0.0002013967,0.002698122,0.01536783,0.0001415602,0.0003636029,0.005005596,0.0001562556,0.04702257,0.604079,0.002495388,0.3220586,0.0004100351],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02343097,0.001511119,0.954293,0.0002075845,0.0006005591,0.0002631921,0.0004708711,0.006617339,0.01260537],"genre_scores_gemma":[0.381461,0.00152296,0.5683098,0.0006253484,0.0004253988,0.0005594609,0.001291768,0.0003460279,0.04545822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006036139,"threshold_uncertainty_score":0.02019286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007062057205626865,"score_gpt":0.213831931741482,"score_spread":0.2067698745358551,"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."}}