{"id":"W4392251305","doi":"10.2139/ssrn.4741566","title":"Developing a Residential Occupancy Schedules Generator Based on Smart Thermostat Data","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Thermostat; Occupancy; Generator (circuit theory); Computer science; Real-time computing; Automotive engineering; Architectural engineering; Electrical engineering; Engineering; Power (physics); Physics","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":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008624777,0.0003429075,0.0004048181,0.0004165338,0.001309477,0.0008777778,0.001943582,0.0003719682,0.0003010428],"category_scores_gemma":[0.0006514713,0.0003210653,0.0002947172,0.000461228,0.0002168895,0.0001426838,0.000559004,0.005223872,0.0001841937],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003466693,"about_ca_system_score_gemma":0.05330802,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007763517,"about_ca_topic_score_gemma":0.1699595,"domain_scores_codex":[0.9944147,0.0009587824,0.0006024992,0.0008736182,0.001067563,0.002082789],"domain_scores_gemma":[0.9980206,0.0002037548,0.0003191272,0.001021445,0.0002630318,0.0001719719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000222018,0.0002835831,0.0025171,0.000273496,0.001959617,0.00005746202,0.003386444,0.004547389,0.00005075154,0.888256,0.001695345,0.09675084],"study_design_scores_gemma":[0.0004465322,0.0001038274,0.0003219724,0.0007605279,0.0006766521,0.000006245643,0.00654477,0.02462756,0.00005378136,0.9515697,0.01397127,0.0009171324],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5827661,0.03548691,0.2971697,0.06654572,0.006659818,0.001989411,0.0004807377,0.000786611,0.008115074],"genre_scores_gemma":[0.9912235,0.003817111,0.0009262426,0.0004721045,0.002127349,0.00002565868,0.0002671919,0.00005308286,0.001087771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4084575,"threshold_uncertainty_score":0.9999907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05171233302705717,"score_gpt":0.3544397986255765,"score_spread":0.3027274655985193,"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."}}