{"id":"W2127562956","doi":"10.1109/cnsr.2009.32","title":"Applications of Wireless Sensor Networks and RFID in a Smart Home Environment","year":2009,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Radio-frequency identification; Wireless sensor network; Computer science; Wireless; Focus (optics); Home automation; Identification (biology); Architecture; Telecommunications; Work (physics); Wireless network; Population; Ubiquitous computing; Smart environment; Computer security; Computer network; Human–computer interaction; Internet of Things; Engineering; Medicine; Geography","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":[0.000545593,0.000377,0.0002178882,0.0005012982,0.000317748,0.000686934,0.0003543477,0.001158973,0.001337711],"category_scores_gemma":[0.0009058926,0.0002041403,0.0002272138,0.0008560932,0.0006258959,0.00160707,0.0007084335,0.000420569,0.0003841631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002150933,"about_ca_system_score_gemma":0.0001588097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004093555,"about_ca_topic_score_gemma":0.0006220505,"domain_scores_codex":[0.9995282,0.0002105322,0.00002760005,0.00005682097,0.0001526938,0.00002422055],"domain_scores_gemma":[0.999448,0.0003230089,0.00004761753,0.00005321621,0.00009821977,0.00002990499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004703151,0.0001619596,0.006015636,0.001073911,0.0001204227,0.003924453,0.001291062,0.08064501,0.06292017,0.122542,0.01159294,0.7092422],"study_design_scores_gemma":[0.00008207541,0.0007815005,0.006633916,0.0005050792,0.0002049264,0.009692361,0.001581482,0.4287065,0.05759618,0.1336926,0.36036,0.0001633322],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.07201565,0.04242272,0.8182569,0.004031292,0.001176864,0.0001155859,0.00005595597,0.001044904,0.06088012],"genre_scores_gemma":[0.6901116,0.04025897,0.2468529,0.001014378,0.001249548,0.0000867238,0.00008432801,0.00007852047,0.02026297],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.001337711,"threshold_uncertainty_score":0.004475057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009779737491766933,"score_gpt":0.2118231839509282,"score_spread":0.2020434464591613,"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."}}