{"id":"W4388320652","doi":"10.1145/3600100.3623718","title":"Overcoming Data Scarcity through Transfer Learning in CO2-Based Building Occupancy Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Aalto-Yliopisto","keywords":"Occupancy; Transfer of learning; Computer science; Hyperparameter; Deep learning; Artificial intelligence; Machine learning; Labeled data; Domain (mathematical analysis); Data modeling; Transfer (computing); Field (mathematics); Database; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008957014,0.0001089047,0.000117045,0.00004342208,0.0002334893,0.0000348433,0.0002603203,0.00006412811,0.0001881534],"category_scores_gemma":[0.0001528659,0.0001084481,0.00002987399,0.0006229151,0.0000578872,0.0004616102,0.0002007881,0.0002663371,0.0001576761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000132873,"about_ca_system_score_gemma":0.000007351229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002488331,"about_ca_topic_score_gemma":0.0003987464,"domain_scores_codex":[0.9986941,0.00009761089,0.000210037,0.0004057128,0.0002604334,0.0003321596],"domain_scores_gemma":[0.9994746,0.000170732,0.00002243571,0.0002882728,0.000002549762,0.00004134394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002645403,0.00003646516,0.8007136,0.00002873247,0.000004955523,0.00001294554,0.0008489842,0.1046339,0.03839058,0.00003299808,0.0001101174,0.05516033],"study_design_scores_gemma":[0.0007389699,0.00006934984,0.3561142,0.00010383,0.00001252147,0.00000321589,0.0007020156,0.5915169,0.04496841,0.0002589583,0.005087833,0.0004238127],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.944087,0.000004972526,0.05390751,0.00009252928,0.0001527955,0.0000872173,0.000003034617,0.0002923817,0.001372513],"genre_scores_gemma":[0.9971904,0.000004402555,0.00247814,0.00003979354,0.00005718007,0.00000599841,0.0000137395,0.0000149192,0.0001954067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.486883,"threshold_uncertainty_score":0.4422387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09104761541906312,"score_gpt":0.3197755938517539,"score_spread":0.2287279784326907,"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."}}