{"id":"W4385076727","doi":"10.1016/j.buildenv.2023.110651","title":"Unsupervised domain adaptation with and without access to source data for estimating occupancy and recognizing activities in smart buildings","year":2023,"lang":"en","type":"article","venue":"Building and Environment","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Occupancy; Domain adaptation; Adaptation (eye); Computer science; Domain (mathematical analysis); Real-time computing; Environmental science; Data mining; Artificial intelligence; Engineering; Architectural engineering; Mathematics; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0007170101,0.0001593502,0.0002156446,0.0001866848,0.0002003153,0.0003859376,0.0002709962,0.00004063774,7.520138e-7],"category_scores_gemma":[0.00005679431,0.0001521787,0.000009551523,0.0001792353,0.00004226288,0.001057428,0.0007226015,0.00007728711,0.00000123846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003866426,"about_ca_system_score_gemma":0.00001450278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001777277,"about_ca_topic_score_gemma":0.00007193271,"domain_scores_codex":[0.9986656,0.00005636362,0.0001886676,0.0006622992,0.0001709426,0.0002560965],"domain_scores_gemma":[0.9991909,0.000297676,0.00008263614,0.0003220875,0.000006528835,0.0001002321],"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":[0.00007160289,0.00003219232,0.1165025,0.0001737791,0.0000361838,0.000004361996,0.005500408,0.001419985,0.003968124,0.0000715124,0.00007380242,0.8721456],"study_design_scores_gemma":[0.002589413,0.0002976647,0.09992868,0.000998219,0.00003528299,0.00007891692,0.002785907,0.8843691,0.000897599,0.001016709,0.006206432,0.0007961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5327908,0.00004339571,0.4663471,0.0004254177,0.00003106282,0.0002943395,0.000008927508,0.00005269167,0.00000633104],"genre_scores_gemma":[0.7885371,0.00003801719,0.2111599,0.00008447774,0.00002916997,0.0001046042,0.000009194298,0.00001544442,0.00002211446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8829491,"threshold_uncertainty_score":0.6205669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08416189069732156,"score_gpt":0.2963959288487572,"score_spread":0.2122340381514357,"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."}}