{"id":"W7011518308","doi":"","title":"Monitoring user behavior : monitoring and analysis of manual control strategies for lighting and blinds","year":2001,"lang":"en","type":"article","venue":"NPARC","topic":"Consumer behavior in food and health","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"GLARE; Control (management); Thermal comfort; Smart lighting; Artificial light; Control system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001117914,0.0004076335,0.0004375458,0.0010471,0.0002342991,0.0004326874,0.0002862192,0.0004265344,0.001308639],"category_scores_gemma":[0.009281548,0.0002132461,0.0002502412,0.0004099493,0.0002748716,0.0004037948,0.0003177756,0.0003490669,0.0003631019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002793851,"about_ca_system_score_gemma":0.0003118113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002317423,"about_ca_topic_score_gemma":0.002766909,"domain_scores_codex":[0.9990175,0.0004693568,0.00008773938,0.0001492166,0.0002092374,0.00006694674],"domain_scores_gemma":[0.9932945,0.004085788,0.001017697,0.0004050606,0.0007218157,0.0004750038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003656458,0.002673071,0.7870123,0.0004909256,0.0001569154,0.0001731883,0.005818705,0.002077843,0.02221426,0.0001605264,0.0004866894,0.1750791],"study_design_scores_gemma":[0.00005934216,0.00286338,0.9787753,0.00002166963,0.00007546096,0.0002658071,0.001193113,0.01108282,0.004936816,0.0001551563,0.0005218406,0.00004942337],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971008,0.00005053946,0.001939085,0.0000128483,0.000002618338,0.00008935411,0.0001168755,0.00006114389,0.0006267539],"genre_scores_gemma":[0.996074,0.00004532894,0.003145841,0.00001505379,0.000004302753,0.00009658458,0.0001705445,0.00000992577,0.0004384441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002317423,"threshold_uncertainty_score":0.005912185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06240479398231499,"score_gpt":0.3969046736838698,"score_spread":0.3344998797015548,"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."}}