{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004850542,0.0004234397,0.0008378787,0.0001900694,0.00001731804,0.00004398066,0.0003281263,0.0005119223,0.0001076827],"category_scores_gemma":[0.00002312829,0.0004548464,0.0002606839,0.000122921,0.00004694886,0.00004038525,0.0003189356,0.0005877158,0.00001052878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003603354,"about_ca_system_score_gemma":0.00005746753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002274347,"about_ca_topic_score_gemma":0.0002402653,"domain_scores_codex":[0.9977063,0.0000756203,0.0009883234,0.000513661,0.0002437524,0.0004723447],"domain_scores_gemma":[0.998722,0.0002129114,0.0001286552,0.000796425,0.00004399314,0.00009601434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001653542,0.00005773342,0.0005874415,0.001560528,0.0001052203,0.00002307629,0.0005065125,0.9921659,0.0009478825,0.00008864081,0.0002804174,0.003660142],"study_design_scores_gemma":[0.0002478769,0.00004855165,0.0003706889,0.0008279962,0.00003572896,0.000008612869,0.0007592543,0.9702417,0.0261968,0.00006434122,0.0005621446,0.0006363165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2280248,0.001170216,0.7662112,0.00003780189,0.002185502,0.001453377,0.00004005645,0.0001838866,0.0006930858],"genre_scores_gemma":[0.9910569,0.000368662,0.007451934,0.0000215448,0.0002940859,0.0004109032,0.00009998798,0.000115956,0.0001800115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7630321,"threshold_uncertainty_score":0.9997903,"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."}}