{"id":"W4389541172","doi":"10.17118/11143/20841","title":"Occupancy detection in a residential building using sensor fusion dataand machine learning algorithms","year":2023,"lang":"en","type":"article","venue":"","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Occupancy; Sensor fusion; Computer science; Information fusion; Fusion; Artificial intelligence; Machine learning; Algorithm; Engineering; Architectural engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000712656,0.0001038777,0.000105433,0.0001048571,0.0002991548,0.00004434036,0.00009164496,0.00005987937,0.0002009361],"category_scores_gemma":[0.0001476643,0.0001008065,0.00003009326,0.0006384504,0.00003669379,0.0002494473,0.0002870282,0.0002255872,0.0001526322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001477396,"about_ca_system_score_gemma":0.000003619681,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007013571,"about_ca_topic_score_gemma":0.0003615004,"domain_scores_codex":[0.998804,0.00009988256,0.0002181952,0.0003071471,0.0002643757,0.0003064138],"domain_scores_gemma":[0.9996908,0.0000675175,0.00006234308,0.0001214831,0.000003328291,0.00005447828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004991291,0.0000331167,0.2781444,0.00002354334,0.000007186913,0.000101407,0.00101748,0.06188491,0.4564052,0.000005580753,0.00005080249,0.2022765],"study_design_scores_gemma":[0.0003223279,0.00003972439,0.06851997,0.00004753025,0.000007175315,0.00002610136,0.0004071472,0.9025869,0.0269648,0.00007281882,0.000801102,0.0002043644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847888,0.000008299424,0.01431357,0.0000326701,0.0002660391,0.00008166856,0.000001847609,0.0001879686,0.0003191662],"genre_scores_gemma":[0.9928643,0.00001114992,0.006253201,0.000007608184,0.0001376203,0.000002708986,0.000006795283,0.0000158449,0.0007007918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.840702,"threshold_uncertainty_score":0.9995988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04934969276182059,"score_gpt":0.3094842732227208,"score_spread":0.2601345804609002,"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."}}