{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009808425,0.0003961622,0.0003082193,0.0005828428,0.0001324706,0.0005015963,0.0002084498,0.0002457443,0.0009539733],"category_scores_gemma":[0.0002359297,0.0001567024,0.0003293699,0.0005793378,0.0001108636,0.0006003716,0.0002378316,0.0002310297,0.0004361868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001131852,"about_ca_system_score_gemma":0.0001030817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001163349,"about_ca_topic_score_gemma":0.002524417,"domain_scores_codex":[0.9998883,0.000007345282,0.000004034421,0.00004770725,0.00004005394,0.00001247272],"domain_scores_gemma":[0.999887,0.00002250968,0.00002740223,0.00001661909,0.00003881438,0.000007612002],"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":[0.0004171626,0.0001806856,0.03069137,0.0002094389,0.0001551808,0.0001266578,0.0001358437,0.01965423,0.642708,0.0005586526,0.000896429,0.3042664],"study_design_scores_gemma":[0.00002504474,0.0002662861,0.219197,0.00001509872,0.0002291072,0.0003291195,0.0002317514,0.5905951,0.1846592,0.001059024,0.003317677,0.00007547337],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7843516,0.0003398079,0.2115028,0.00004299547,0.00003567892,0.00002067406,0.0003093816,0.0005510637,0.002846014],"genre_scores_gemma":[0.9649096,0.0001437496,0.03262403,0.00001389326,0.00001091722,0.000009014764,0.000225847,0.00004099641,0.002022083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001163349,"threshold_uncertainty_score":0.003191352,"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."}}