{"id":"W2057996605","doi":"10.1094/cchem.2003.80.3.285","title":"Color Calibration of Scanners for Scanner‐Independent Grain Grading","year":2003,"lang":"en","type":"article","venue":"Cereal Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Saskatchewan Pulse Growers","keywords":"Scanner; Artificial intelligence; Computer vision; RGB color model; Histogram; Computer science; Calibration; Color correction; Grayscale; Color balance; Pattern recognition (psychology); Color image; Image processing; Mathematics; Pixel; Statistics; Image (mathematics)","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":[0.002840537,0.0004820984,0.0003291492,0.0006491244,0.0002406885,0.0005174408,0.0008688212,0.0004795621,0.001209436],"category_scores_gemma":[0.008531237,0.000537335,0.0003151576,0.0006631902,0.0004003547,0.0005424432,0.0004961672,0.0004963902,0.0004735782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006428429,"about_ca_system_score_gemma":0.0004687253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001316457,"about_ca_topic_score_gemma":0.002022994,"domain_scores_codex":[0.9980381,0.0005907427,0.00008577097,0.0002534594,0.000951057,0.00008077394],"domain_scores_gemma":[0.9953194,0.001868904,0.0004948848,0.0008863953,0.001374171,0.00005615022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005879122,0.0001213761,0.01181757,0.0001433339,0.00005746853,0.00006080018,0.0001568012,0.01321317,0.8667945,0.00107212,0.0007224454,0.1052524],"study_design_scores_gemma":[0.00003472401,0.0002830317,0.01216003,0.00001006085,0.00006620066,0.0002145689,0.00003849996,0.07534461,0.909356,0.0003452584,0.002108745,0.0000383503],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5501546,0.0004592614,0.4449158,0.0001306402,0.00009493224,0.0001777069,0.0001261971,0.002397198,0.001543629],"genre_scores_gemma":[0.8472424,0.0001620793,0.1512665,0.00004505398,0.000008622138,0.00006462115,0.0001292136,0.0002313425,0.0008501791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002840537,"threshold_uncertainty_score":0.01502234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620229563003978,"score_gpt":0.2658027300266569,"score_spread":0.2496004343966172,"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."}}