{"id":"W2965091855","doi":"10.1109/isie.2019.8781092","title":"On the Occupancy Measurement and Analysis for Residential Applications","year":2019,"lang":"en","type":"article","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Occupancy; Occupancy grid mapping; Computer science; Principal component analysis; Energy consumption; Wavelet; Energy (signal processing); Field (mathematics); Data mining; Statistics; Artificial intelligence; Engineering; Mathematics","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":[0.0007151234,0.0004411427,0.0003933689,0.0005464869,0.000255632,0.0006514517,0.0005408062,0.0003769701,0.001768863],"category_scores_gemma":[0.002136016,0.0002389624,0.0002898034,0.00132859,0.0003336479,0.0007756497,0.0003899428,0.000395929,0.0008453022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003004044,"about_ca_system_score_gemma":0.0003564228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002174964,"about_ca_topic_score_gemma":0.002378798,"domain_scores_codex":[0.9989194,0.0003920498,0.00004124654,0.0001304796,0.0004657416,0.00005120317],"domain_scores_gemma":[0.9992552,0.0002918215,0.00007489044,0.0001096167,0.0002448102,0.00002354057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003221668,0.0002041006,0.009078651,0.0004403961,0.00008109223,0.0002207477,0.0002850358,0.1078426,0.07927256,0.01932324,0.005243183,0.7776863],"study_design_scores_gemma":[0.00001087008,0.0002843192,0.01491614,0.00005940125,0.00004128274,0.0003754414,0.0001694104,0.9133404,0.04182732,0.006891835,0.02202964,0.00005398965],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04097372,0.001099019,0.951516,0.0002892762,0.00007815678,0.00007312618,0.0001866675,0.0009953943,0.004788669],"genre_scores_gemma":[0.7127959,0.001771146,0.2806037,0.0001319179,0.0001575041,0.0001392916,0.0004713712,0.0001890579,0.003740068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002174964,"threshold_uncertainty_score":0.00591743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01407761108552714,"score_gpt":0.2027946634164516,"score_spread":0.1887170523309245,"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."}}