{"id":"W4396505468","doi":"10.1109/tip.2024.3393390","title":"Learning to Recover Spectral Reflectance From RGB Images","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; University of Manitoba","keywords":"RGB color model; Artificial intelligence; Computer science; Computer vision; Ground truth; Reflectivity; Pattern recognition (psychology); Optics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002285268,0.000257763,0.0001886136,0.0003401421,0.0003447158,0.001403084,0.000596175,0.00006769183,0.0001114195],"category_scores_gemma":[0.00001389394,0.0002614253,0.0001020222,0.001011918,0.00005056771,0.002407528,0.000007191998,0.0006275219,0.000435976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001987302,"about_ca_system_score_gemma":0.0001287402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003093562,"about_ca_topic_score_gemma":0.000009671965,"domain_scores_codex":[0.9980564,0.00006405789,0.000284544,0.0007995238,0.0003634692,0.0004320824],"domain_scores_gemma":[0.9992758,0.0000961855,0.00004796528,0.0003779985,0.00009594813,0.0001060799],"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":[0.00001674902,0.00005729458,0.000001255128,0.00006009586,0.00001929469,0.00007490251,0.001635622,0.0006018424,0.4582592,0.00002555236,0.0009686141,0.5382796],"study_design_scores_gemma":[0.0001035678,0.0001648927,0.00001842491,0.000498603,0.00001880757,0.00001602872,0.00004810456,0.06229271,0.9334103,0.001228544,0.001824187,0.0003758006],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003730757,0.0003449467,0.9877011,0.001182947,0.0006684346,0.0002049698,0.000008253,0.002515959,0.003642588],"genre_scores_gemma":[0.6479464,0.00003685531,0.3494506,0.0003144,0.0000999186,0.0000690153,0.000001038334,0.00003700098,0.002044803],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6442156,"threshold_uncertainty_score":0.9999838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255581173888426,"score_gpt":0.2886021015375174,"score_spread":0.2760462897986332,"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."}}