{"id":"W2924325937","doi":"10.1016/j.infrared.2019.03.033","title":"Single kernel wheat hardness estimation using near infrared hyperspectral imaging","year":2019,"lang":"en","type":"article","venue":"Infrared Physics & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Canada Foundation for Innovation","keywords":"Hyperspectral imaging; Partial least squares regression; Principal component analysis; Calibration; Kernel (algebra); Mathematics; Smoothing; Mean squared error; Pattern recognition (psychology); Biological system; Artificial intelligence; Computer science; Materials science; Statistics","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"],"consensus_categories":[],"category_scores_codex":[0.0000472281,0.0003287806,0.0004716331,0.0002505491,0.0001800367,0.0001270603,0.0005005628,0.0003057731,0.0006555401],"category_scores_gemma":[0.00007802159,0.0003678372,0.000145928,0.001401862,0.0002552227,0.0003999413,0.0001802821,0.0005041236,0.0001778397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003980033,"about_ca_system_score_gemma":0.0001187781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004727723,"about_ca_topic_score_gemma":5.172355e-7,"domain_scores_codex":[0.998272,0.000009921395,0.0003456718,0.0005433695,0.0002534098,0.0005756367],"domain_scores_gemma":[0.9987038,0.00004655219,0.0002251005,0.0008142226,0.0001412718,0.00006899588],"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":[0.00002819971,0.0002374683,0.02399878,0.000119337,0.0001529467,0.00001646706,0.0002452749,0.001127852,0.9654251,0.002286196,0.0003099839,0.00605234],"study_design_scores_gemma":[0.0007509094,0.00003298816,0.00009763631,0.00006237319,0.0001730262,0.00004353018,0.0007760445,0.07214049,0.8868823,0.03773629,0.0008087858,0.0004956146],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9603259,0.0005858439,0.01221656,0.0001800394,0.0001991516,0.0001183531,0.0000243532,0.0008540876,0.02549571],"genre_scores_gemma":[0.9859819,0.000008933981,0.01230173,0.00008639804,0.0001210235,0.00001552786,0.00005458752,0.00006609881,0.001363748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07854284,"threshold_uncertainty_score":0.9998773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01416068469081155,"score_gpt":0.2627358086209086,"score_spread":0.248575123930097,"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."}}