{"id":"W2026025713","doi":"10.1002/col.20649","title":"A Monte Carlo method for assessing color rendering quality with possible application to color rendering standards","year":2010,"lang":"en","type":"article","venue":"Color Research & Application","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Standards and Technology","keywords":"Color rendering index; Rendering (computer graphics); Computer science; High color; Spectral power distribution; Color temperature; Artificial intelligence; Color difference; Monte Carlo method; RGB color model; Computer vision; Statistics; Mathematics; Light-emitting diode; Optics; Color image; Image processing; 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.004786998,0.0002584684,0.0003493539,0.0002499144,0.001340944,0.0005055914,0.0007658349,0.0001070899,0.00003441868],"category_scores_gemma":[0.0001301815,0.0002430342,0.0001001071,0.001630954,0.0001952932,0.0004533116,0.0002392242,0.0006013601,0.00003613294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003230703,"about_ca_system_score_gemma":0.0007222457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001432763,"about_ca_topic_score_gemma":0.001214427,"domain_scores_codex":[0.9964386,0.0001446589,0.0005031592,0.001078311,0.001006369,0.0008288638],"domain_scores_gemma":[0.996214,0.0006669945,0.000245673,0.001057207,0.001435545,0.0003805915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003424147,0.0003845846,0.003673621,0.00008688619,0.00005860894,3.373733e-7,0.0009560139,0.007785568,0.6857626,0.07910638,0.001465525,0.2203775],"study_design_scores_gemma":[0.002444508,0.0007490106,0.02695291,0.00008474112,0.00009572334,0.000005706924,0.005637557,0.445638,0.1351285,0.01261184,0.3693062,0.001345243],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4790955,0.000006183524,0.513675,0.001815657,0.0000322311,0.00324112,0.000134977,0.00009521009,0.001904075],"genre_scores_gemma":[0.909146,0.000001594433,0.07365458,0.00005711601,0.0003226621,0.01633976,0.00005823328,0.00004603748,0.0003740788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.550634,"threshold_uncertainty_score":0.9999592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06587657260029134,"score_gpt":0.5006679348273335,"score_spread":0.4347913622270422,"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."}}