{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001594924,0.0003139144,0.0004409317,0.0006981872,0.0003574869,0.0007839327,0.0004451223,0.0007036529,0.001311257],"category_scores_gemma":[0.0006672554,0.0002384501,0.0001947292,0.0007907953,0.0002044578,0.0009010925,0.0008215514,0.0002925101,0.0005830528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002213199,"about_ca_system_score_gemma":0.0002251953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003497722,"about_ca_topic_score_gemma":0.005008632,"domain_scores_codex":[0.9997937,0.00002499119,0.00001006215,0.00007762097,0.00006446872,0.00002924356],"domain_scores_gemma":[0.9997579,0.00006240979,0.00003749117,0.0000485162,0.00005296736,0.00004068777],"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":[0.001507724,0.0004836582,0.06848813,0.0002170465,0.0002102263,0.003254079,0.002344898,0.1197203,0.1522671,0.006237131,0.008074083,0.6371956],"study_design_scores_gemma":[0.00003413452,0.0003789687,0.04900411,0.00003461721,0.0001451217,0.001280815,0.001390453,0.8941442,0.03838391,0.00547569,0.009673509,0.00005452944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6071566,0.0003695269,0.381952,0.0002370591,0.0001175031,0.00005409744,0.0003901108,0.002027886,0.007695164],"genre_scores_gemma":[0.9309413,0.0001919358,0.06302401,0.00005520754,0.00004816504,0.00001492117,0.0002345163,0.00004973122,0.005440211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003497722,"threshold_uncertainty_score":0.00695473,"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."}}