{"id":"W2581428794","doi":"10.1109/access.2017.2653079","title":"SITE: The Simple Internet of Things Enabler for Smart Homes","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Enabling; Computer science; Usability; Simple (philosophy); Smart objects; The Internet; User interface; Human–computer interaction; Home automation; Control (management); World Wide Web; Computer security; Telecommunications; Artificial intelligence","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":[0.0005418476,0.0006187299,0.0003142848,0.0005736541,0.00037663,0.001025294,0.0009486665,0.0008971603,0.01118],"category_scores_gemma":[0.001185088,0.0002491527,0.0003785195,0.0003947252,0.0005315652,0.001852096,0.001600927,0.0006611355,0.003459298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002184242,"about_ca_system_score_gemma":0.0002699405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005029581,"about_ca_topic_score_gemma":0.001003092,"domain_scores_codex":[0.999419,0.0001274374,0.00004038957,0.00005525508,0.0002913345,0.00006663859],"domain_scores_gemma":[0.9995856,0.000109112,0.00004725459,0.0001002518,0.00008502753,0.0000726506],"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":[0.001200754,0.0005205299,0.005899746,0.001789184,0.00008772632,0.002453867,0.002284052,0.00304284,0.1265838,0.07319768,0.1254279,0.6575119],"study_design_scores_gemma":[0.0002213318,0.001124308,0.009003854,0.0003084717,0.0001032026,0.004230791,0.000471396,0.01530126,0.05920785,0.01223719,0.8975731,0.0002172556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1228509,0.002386326,0.6516762,0.001605625,0.0009592726,0.001575784,0.002530668,0.08130421,0.1351109],"genre_scores_gemma":[0.6067997,0.002149412,0.2800958,0.002265559,0.0003917615,0.001228413,0.004719126,0.003722149,0.09862804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01118,"threshold_uncertainty_score":0.03740078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0744808304534464,"score_gpt":0.336981431479584,"score_spread":0.2625006010261376,"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."}}