{"id":"W4377988201","doi":"10.1002/col.22862","title":"Spectral reflectance estimation from non‐raw color images with nonlinearity correction","year":2023,"lang":"en","type":"article","venue":"Color Research & Application","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Color correction; Artificial intelligence; Computer science; Radiance; Computer vision; Nonlinear system; Color balance; Spectral signature; Spectral color; Color image; Mathematics; Algorithm; Image (mathematics); Color space; Remote sensing; Color model; 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":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007876788,0.0008389269,0.0004094683,0.0007238155,0.0002674349,0.0006930276,0.0006150796,0.0005229982,0.0008915357],"category_scores_gemma":[0.002107105,0.000224726,0.000731404,0.000592828,0.0004651798,0.001086947,0.0004983765,0.0006514094,0.0006325919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004626078,"about_ca_system_score_gemma":0.0006920407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003517796,"about_ca_topic_score_gemma":0.002677351,"domain_scores_codex":[0.9995387,0.00008659165,0.00002227865,0.0001193211,0.0001924263,0.00004072213],"domain_scores_gemma":[0.9992536,0.0002040159,0.00008889951,0.0001431531,0.0002887396,0.00002163804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005881153,0.0002755852,0.008188641,0.0004274845,0.0001572077,0.0002023444,0.000198985,0.3895073,0.168019,0.004253347,0.00192781,0.4262541],"study_design_scores_gemma":[0.00001053476,0.00004496344,0.001533922,0.000007412796,0.00002010887,0.00005376474,0.0000199043,0.95805,0.03906872,0.0006456985,0.0005270066,0.00001788817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1785364,0.0003038836,0.8163995,0.0001474719,0.00008148577,0.00005258388,0.00008619969,0.001523237,0.002869151],"genre_scores_gemma":[0.806255,0.0002964342,0.190838,0.00009153768,0.0000221413,0.00004515017,0.0002254656,0.0001473232,0.002079081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003517796,"threshold_uncertainty_score":0.006994665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03503029299233091,"score_gpt":0.3942855578885286,"score_spread":0.3592552648961977,"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."}}