{"id":"W2132865445","doi":"10.1191/1365782805li132oa","title":"Lighting quality research using rendered images of offices","year":2005,"lang":"en","type":"article","venue":"Lighting Research & Technology","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Luminance; Brightness; Artificial intelligence; Computer vision; Illuminance; Computer science; Set (abstract data type); Image quality; Attractiveness; Mathematics; Image (mathematics); Optics; Psychology; 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":[],"consensus_categories":[],"category_scores_codex":[0.009313378,0.0001725986,0.00037895,0.0009129692,0.0009594796,0.0000518348,0.001131317,0.0003774792,0.0006533591],"category_scores_gemma":[0.001635598,0.0001578215,0.00008291818,0.003068065,0.001978982,0.0003044883,0.00159126,0.001718285,0.0004293652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006355041,"about_ca_system_score_gemma":0.0001315998,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006877991,"about_ca_topic_score_gemma":0.003351848,"domain_scores_codex":[0.9945041,0.0007751844,0.0006566194,0.0007536463,0.001678806,0.001631622],"domain_scores_gemma":[0.9976877,0.0007794863,0.0002057516,0.0009427485,0.0002073278,0.000176997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007035143,0.0003592824,0.4269604,0.0001056359,0.0000340821,0.00002715763,0.001761061,0.0001633053,0.5138535,0.003807744,0.01080598,0.04205153],"study_design_scores_gemma":[0.001722962,0.001430714,0.0672714,0.0005943463,0.00002560263,0.00007503085,0.01266159,0.003743464,0.8025296,0.01780256,0.0910393,0.001103382],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97402,0.0008606737,0.00012125,0.01191557,0.00003650006,0.0003945815,0.000006725766,0.0001839923,0.01246071],"genre_scores_gemma":[0.9857624,0.0000905729,0.01219821,0.00003162334,0.0001685159,0.00002086793,0.000002108883,0.0000317033,0.001694026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.359689,"threshold_uncertainty_score":0.9997353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1983410106929944,"score_gpt":0.4765859465069606,"score_spread":0.2782449358139661,"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."}}