{"id":"W4250782293","doi":"10.32920/ryerson.14646186","title":"Smartlife: A Point of Intelligence for Wireless Sensor Networks in Ubiquitous Environment","year":2021,"lang":"en","type":"preprint","venue":"","topic":"IoT-based Smart Home Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"CMC Microsystems","keywords":"Computer science; Embedded system; Ubiquitous computing; Smart environment; Architecture; Field-programmable gate array; Wireless; Home automation; Wireless sensor network; Ambient intelligence; Computer security; Operating system; Human–computer interaction; Internet of Things","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":[0.0004147753,0.0004290998,0.0002318208,0.000453518,0.0003759303,0.001581706,0.0006608265,0.0008915353,0.006507259],"category_scores_gemma":[0.0006991006,0.0002992398,0.0001635618,0.0004202363,0.000739033,0.002674374,0.0009255176,0.000969593,0.002491708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003740692,"about_ca_system_score_gemma":0.0003885177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003496064,"about_ca_topic_score_gemma":0.0005273384,"domain_scores_codex":[0.9995384,0.00009384744,0.00003386039,0.00007309348,0.0002138119,0.00004707928],"domain_scores_gemma":[0.9997842,0.0000381592,0.00002022442,0.00005122219,0.00006724957,0.00003895263],"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":[0.0003031621,0.0001117998,0.001926161,0.0007057111,0.0000506075,0.0009819984,0.001089127,0.008065986,0.06475555,0.3876177,0.05296921,0.4814228],"study_design_scores_gemma":[0.00005204772,0.000392239,0.001302846,0.0001745081,0.00003981468,0.001573582,0.0002398556,0.05385596,0.05349335,0.0632494,0.8255737,0.00005272778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01922598,0.003062927,0.9080796,0.001780466,0.0006105401,0.0002366734,0.0001836997,0.009116088,0.05770401],"genre_scores_gemma":[0.2860218,0.00477314,0.5995579,0.00137479,0.0005270859,0.0003568823,0.001020906,0.001277102,0.1050904],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006507259,"threshold_uncertainty_score":0.02176893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0129625723080986,"score_gpt":0.2120085866883476,"score_spread":0.199046014380249,"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."}}