{"id":"W4386432560","doi":"10.1007/978-981-19-9822-5_106","title":"Deep Learning Models for Future Occupancy Prediction in Residential Buildings","year":2023,"lang":"en","type":"book-chapter","venue":"Environmental science and engineering","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Occupancy; Computer science; Deep learning; Artificial intelligence; Viewpoints; Sequence (biology); Multilayer perceptron; Machine learning; Perceptron; Recurrent neural network; Term (time); Artificial neural network; Construct (python library); Long short term memory; Pattern recognition (psychology); 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":[0.0003082952,0.0001583155,0.0001213739,0.0003402624,0.0001699331,0.0001176955,0.0002179577,0.0001101242,0.00001135855],"category_scores_gemma":[0.00001292897,0.0001751054,0.00004189798,0.0001124095,0.00005796087,0.0007710133,0.0001638149,0.0002460709,0.00002059688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000166639,"about_ca_system_score_gemma":0.00001589038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003108148,"about_ca_topic_score_gemma":0.000003524774,"domain_scores_codex":[0.9987627,0.000002405304,0.000173214,0.0004683001,0.000354883,0.0002385335],"domain_scores_gemma":[0.9997211,0.00002205329,0.00005073196,0.0001165967,0.000007566727,0.00008197156],"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":[0.00003292133,0.00004388317,0.0001931396,0.0002431177,0.00003592626,0.0000357763,0.003164976,0.176246,0.015588,0.08966254,0.0003199519,0.7144338],"study_design_scores_gemma":[0.0002240474,0.00007607671,0.001414079,0.00009746511,0.000006794746,0.00001752443,0.0000415132,0.9851766,0.0002109217,0.004126593,0.008333567,0.0002748749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01548465,0.00130706,0.9572161,0.0001906014,0.005640281,0.001150685,0.00003752871,0.0009008624,0.01807223],"genre_scores_gemma":[0.9225908,0.004360549,0.01617188,0.00009151838,0.001774678,0.0002090099,0.00007976069,0.0001502769,0.05457151],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9410442,"threshold_uncertainty_score":0.7140592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01299913745571064,"score_gpt":0.1899004362065395,"score_spread":0.1769012987508289,"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."}}