{"id":"W3212779617","doi":"10.1002/col.22749","title":"Using smooth metamers to estimate color appearance metrics for diverse <scp>color‐normal</scp> observers","year":2021,"lang":"en","type":"article","venue":"Color Research & Application","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Eye Institute","keywords":"Color space; Artificial intelligence; Color difference; Observer (physics); Mathematics; Computer vision; Color vision; Perception; Stimulus (psychology); Pattern recognition (psychology); Computer science; Psychology; Image (mathematics); Cognitive psychology","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":[0.001051467,0.0003225558,0.0002175421,0.001633374,0.000191946,0.0006070088,0.000369382,0.0002711492,0.001745882],"category_scores_gemma":[0.004554885,0.0002071802,0.0003604474,0.0005465334,0.0002469495,0.0006420051,0.0006929474,0.0003397106,0.0004419439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003861125,"about_ca_system_score_gemma":0.0002609038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003092969,"about_ca_topic_score_gemma":0.003897014,"domain_scores_codex":[0.9996969,0.00008774113,0.00001761382,0.00009171677,0.00008021956,0.00002586553],"domain_scores_gemma":[0.998876,0.0003466881,0.0001310544,0.0002739666,0.0002991355,0.00007313753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001340227,0.0001440358,0.07918237,0.0002396253,0.0002328876,0.0001590662,0.0009602202,0.03694707,0.2617387,0.007166525,0.002872304,0.6090168],"study_design_scores_gemma":[0.0000867475,0.0004446149,0.2441915,0.00004458256,0.0001296537,0.0005442252,0.00042041,0.6575462,0.08638461,0.006777566,0.003268742,0.0001611245],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4615543,0.00008839276,0.5325068,0.0000633797,0.00001270244,0.0001138231,0.0003634114,0.002943176,0.00235394],"genre_scores_gemma":[0.8273183,0.00006103019,0.1712226,0.00002846212,0.000005431853,0.00006013193,0.000491461,0.0002289452,0.0005836063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003092969,"threshold_uncertainty_score":0.006149948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1506835212230586,"score_gpt":0.4512933524285869,"score_spread":0.3006098312055283,"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."}}