{"id":"W3121417500","doi":"10.20944/preprints201705.0031.v2","title":"Integrated Lighting Efficiency Analysis in Large Industrial Buildings to Enhance Indoor Environmental Quality","year":2017,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"North York General Hospital","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; University of Toronto; Slovenská Akadémia Vied","keywords":"Skylight; Daylight; Architectural engineering; Quality (philosophy); Daylighting; Environmental quality; Environmental science; Productivity; Component (thermodynamics); Artificial light; Efficient energy use; Civil engineering; Engineering; Mechanical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002528946,0.0004027301,0.0004389652,0.0007036616,0.0001857618,0.0004793612,0.0002605673,0.0002103978,0.001071633],"category_scores_gemma":[0.0003089923,0.0001615549,0.0003366064,0.0008056667,0.0001314528,0.0002247427,0.0002666454,0.0001446976,0.0001828191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002927118,"about_ca_system_score_gemma":0.0001427323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002183921,"about_ca_topic_score_gemma":0.003645528,"domain_scores_codex":[0.9998019,0.00003463014,0.000007084327,0.00005327594,0.00007142154,0.00003174723],"domain_scores_gemma":[0.999822,0.00005485184,0.00002344889,0.00002353017,0.00006106172,0.00001522603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001284051,0.0006832391,0.1562877,0.0005342494,0.0002094628,0.0003741037,0.00076781,0.147892,0.4770514,0.0005890839,0.001186159,0.2131408],"study_design_scores_gemma":[0.00003890384,0.0005199679,0.5462587,0.00002183681,0.0001485803,0.0001446978,0.0005311561,0.3428855,0.1072214,0.000556962,0.001608847,0.00006339576],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713684,0.0001076173,0.02675365,0.000009893613,0.00000531814,0.00001651293,0.0001751148,0.0002670566,0.001296494],"genre_scores_gemma":[0.9945693,0.0000339,0.005038323,0.000002648118,0.000002477543,0.000008265803,0.0001288111,0.00002360874,0.0001926488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002183921,"threshold_uncertainty_score":0.004342377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06359906617661376,"score_gpt":0.3272639762969305,"score_spread":0.2636649101203168,"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."}}