{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004166333,0.0005507392,0.0006351126,0.001621836,0.0005854752,0.001363086,0.001330783,0.001091279,0.002906068],"category_scores_gemma":[0.01343644,0.0004920562,0.0007665126,0.001039439,0.001090435,0.0008003208,0.001036264,0.001139309,0.0005873344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009499238,"about_ca_system_score_gemma":0.00110887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003971721,"about_ca_topic_score_gemma":0.003467295,"domain_scores_codex":[0.9981644,0.0008988568,0.00007289046,0.000190755,0.000609074,0.00006396614],"domain_scores_gemma":[0.993982,0.004262744,0.0003932921,0.000456842,0.0007783344,0.0001266956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001705006,0.0001237723,0.004025567,0.0001067151,0.00007430769,0.000129453,0.0001361713,0.7939156,0.008769742,0.08103994,0.00119673,0.1103116],"study_design_scores_gemma":[0.000006264702,0.0000144151,0.0001653139,0.000006808868,0.000004175775,0.00002886318,0.000003584529,0.9939188,0.0008436581,0.004526539,0.0004718961,0.00000966351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003991861,0.0000512976,0.9948296,0.00003505299,0.00001532696,0.00003851353,0.00001088635,0.000227463,0.0007999524],"genre_scores_gemma":[0.1509101,0.0001140417,0.84703,0.00006077246,0.00002691936,0.0002167426,0.00006276895,0.0001365251,0.001442082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004166333,"threshold_uncertainty_score":0.02203393,"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."}}