{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007662355,0.001341508,0.0006999176,0.0005663289,0.0003043273,0.0006611254,0.001564824,0.0009434282,0.001491469],"category_scores_gemma":[0.002149055,0.0005253632,0.0008147897,0.0005174458,0.0008102449,0.001562653,0.001053585,0.001487281,0.001221416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000475718,"about_ca_system_score_gemma":0.0005290214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002297245,"about_ca_topic_score_gemma":0.003602271,"domain_scores_codex":[0.9996226,0.00006826708,0.00001052463,0.0001738584,0.00007967572,0.00004502004],"domain_scores_gemma":[0.9995646,0.0001206185,0.00006235686,0.0001401386,0.00009225005,0.0000200569],"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":[0.0002172188,0.0001961856,0.001620382,0.000285918,0.0001269061,0.0001585592,0.0001467566,0.3430851,0.07799047,0.005771999,0.004091489,0.5663091],"study_design_scores_gemma":[0.000003945733,0.00004210793,0.0003955691,0.00001206866,0.00001458772,0.00007767195,0.00001501323,0.9816156,0.01395363,0.002697808,0.001160413,0.00001169032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02482056,0.0003264345,0.9700965,0.0001529657,0.00005534679,0.00004336603,0.0001039856,0.002199831,0.002201027],"genre_scores_gemma":[0.4864965,0.0007422999,0.5033978,0.0004256658,0.0001242484,0.0001296026,0.0008137322,0.0005562744,0.007314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002297245,"threshold_uncertainty_score":0.004989445,"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."}}