{"id":"W2810060291","doi":"10.1016/j.eswa.2018.07.009","title":"Tracking objects within a smart home","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Trilateration; Random forest; Software deployment; Real-time computing; Tracking system; Ground truth; Radio-frequency identification; Classifier (UML); Data mining; Software; Artificial intelligence; Process (computing); Context (archaeology); Video tracking; Kalman filter; Object (grammar); Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00007230168,0.0001511777,0.0001561548,0.0001150219,0.0001895421,0.00007396557,0.0002053898,0.00010304,0.00001442182],"category_scores_gemma":[0.000007343196,0.0001250095,0.00002256186,0.000449478,0.000110056,0.0001150591,0.00001593985,0.00009389633,0.0002687098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000643739,"about_ca_system_score_gemma":0.00002054456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004439331,"about_ca_topic_score_gemma":0.00004843485,"domain_scores_codex":[0.9992004,0.00001027258,0.0002276903,0.0001960953,0.0001461505,0.0002194011],"domain_scores_gemma":[0.9993305,0.00002214433,0.0000423433,0.0004366287,0.000112743,0.00005567974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000144881,0.0006061221,0.01708959,0.001713396,0.001542967,0.00005075072,0.07293598,0.0635232,0.1226213,0.5763273,0.09843063,0.04501389],"study_design_scores_gemma":[0.002027517,0.0004105256,0.001496191,0.00055755,0.00007247389,0.000335952,0.01536218,0.1526479,0.2026047,0.001211071,0.6208112,0.002462811],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01344801,0.001976981,0.9537659,0.00008430237,0.0006205447,0.001322211,0.00001480286,0.004584968,0.02418226],"genre_scores_gemma":[0.995851,0.00001767782,0.002198452,0.00004492595,0.0003082557,0.001286556,0.00001204209,0.00004723602,0.0002338336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.982403,"threshold_uncertainty_score":0.5097738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109292630407301,"score_gpt":0.2251402570885017,"score_spread":0.2142109940477716,"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."}}