{"id":"W1551408431","doi":"","title":"The objective measurement of alpha-amylase in wheat kernels using spectral imaging","year":2010,"lang":"en","type":"article","venue":"World Automation Congress","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Starch; Hyperspectral imaging; Kernel (algebra); Germination; Amylase; Multispectral image; Alpha-amylase; Biological system; Alpha (finance); Chemistry; Mathematics; Food science; Artificial intelligence; Computer science; Enzyme; Horticulture; Biology; Statistics; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003692954,0.0001412764,0.0002024071,0.0002167031,0.0001383545,0.00007183704,0.0002187108,0.00003808883,0.0006540558],"category_scores_gemma":[0.0002012363,0.000113291,0.00008031613,0.0006090658,0.000157408,0.0001563474,0.00003751604,0.0002736148,0.000006314823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001867028,"about_ca_system_score_gemma":0.0001022811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002279883,"about_ca_topic_score_gemma":0.0013735,"domain_scores_codex":[0.9987511,0.0000241336,0.0003619576,0.0002076182,0.0004058338,0.0002493661],"domain_scores_gemma":[0.9991285,0.000134894,0.0002280819,0.0002878933,0.0001738582,0.0000467555],"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":[0.00003121547,0.0001054758,0.04516605,0.00005313211,0.00008484118,0.000007911022,0.0002551936,0.0001493803,0.9491946,0.001294384,0.0002695984,0.003388201],"study_design_scores_gemma":[0.0004780414,0.000003174934,0.01235548,0.00005547456,0.00006701864,0.000006570404,0.0003580088,0.02065583,0.964887,0.0004706396,0.0005086469,0.000154123],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840229,0.0004030455,0.0002331677,0.0003675256,0.0003642221,0.00009921744,0.000007773605,0.00008373564,0.01441837],"genre_scores_gemma":[0.9988837,0.000007077673,0.0003371885,0.00002128189,0.00009693798,0.00001134122,0.00000299708,0.00001382615,0.000625618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03281057,"threshold_uncertainty_score":0.7161452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01785116159628859,"score_gpt":0.2875978696934591,"score_spread":0.2697467080971705,"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."}}