{"id":"W1996571108","doi":"10.1145/1670671.1670677","title":"Comparing lighting quality evaluations of real scenes with those from high dynamic range and conventional images","year":2010,"lang":"en","type":"article","venue":"ACM Transactions on Applied Perception","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Council Canada; Istanbul Teknik Üniversitesi","keywords":"Luminance; High dynamic range; Computer vision; Brightness; Artificial intelligence; Daylight; Computer science; GLARE; Range (aeronautics); Computer graphics (images); Dynamic range; Optics; Engineering; Physics; Materials science","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":[0.001646005,0.0003810146,0.0002121275,0.0004662521,0.0001972764,0.0006589291,0.0001728599,0.000357399,0.002698476],"category_scores_gemma":[0.01046854,0.000179998,0.000421842,0.0002056639,0.0003783361,0.0005524721,0.0005391455,0.0002421966,0.0001904774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000200234,"about_ca_system_score_gemma":0.00009237637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006407202,"about_ca_topic_score_gemma":0.001286391,"domain_scores_codex":[0.9992429,0.0002573295,0.00008345307,0.0001025784,0.0002434744,0.00007024894],"domain_scores_gemma":[0.9948478,0.002482611,0.0009267576,0.0002766638,0.001098523,0.0003677691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01703856,0.002120611,0.5426072,0.002173051,0.0008369835,0.001191238,0.03345882,0.004424905,0.2499011,0.0008411356,0.001668571,0.1437378],"study_design_scores_gemma":[0.0003728648,0.007050546,0.9626249,0.00005579188,0.0001940943,0.0007671022,0.008536447,0.004248855,0.01416542,0.0003616654,0.001511161,0.0001111592],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987683,0.00003674679,0.0004826136,0.000005240267,0.000003940012,0.00002992195,0.00002200941,0.00000578649,0.0006454533],"genre_scores_gemma":[0.9985093,0.0000376715,0.001062486,0.000009417345,0.000006460557,0.00002975545,0.00007428716,0.000005246038,0.00026536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002698476,"threshold_uncertainty_score":0.009027302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01960302791516765,"score_gpt":0.2738375596513397,"score_spread":0.254234531736172,"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."}}