{"id":"W3005922157","doi":"10.1002/fsn3.1436","title":"A flavor uniformity evaluation and improvement of Chinese spirit by electronic nose","year":2020,"lang":"en","type":"article","venue":"Food Science & Nutrition","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Ste. Anne's Hospital","funders":"Beijing Advanced Innovation Center for Food Nutrition and Human Health; Government of Jiangsu Province","keywords":"Electronic nose; Flavor; Homogeneity (statistics); Minification; Computer science; Process engineering; Mathematics; Biochemical engineering; Artificial intelligence; Machine learning; Chemistry; Food science; Engineering; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.00008580625,0.00007708211,0.0000885738,0.00004276412,0.00003983949,0.00001334212,0.0001337353,0.00003855511,0.000004919462],"category_scores_gemma":[0.0001525708,0.00007226352,0.00001553412,0.0005138893,0.0001416547,0.0002714963,0.00003419605,0.00009827045,0.000001374821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001243041,"about_ca_system_score_gemma":0.000009085615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001284611,"about_ca_topic_score_gemma":0.000002408499,"domain_scores_codex":[0.9992363,0.000002617572,0.0001269558,0.0001670226,0.0002679368,0.0001991003],"domain_scores_gemma":[0.9997511,0.00001021761,0.00002936635,0.00009486328,0.00006640532,0.00004807246],"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":[0.00000516794,0.00001918021,0.00007949575,0.00005657299,0.000002100042,5.110236e-8,0.00003629107,0.0002218651,0.9895393,0.00009085887,0.00003038024,0.009918797],"study_design_scores_gemma":[0.0004052154,0.0004983334,0.0001771993,0.00001216269,0.00000510063,7.686519e-7,0.00006648943,0.03219078,0.9589301,0.007442839,0.0001850941,0.00008590888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972489,0.0006751542,0.001100999,0.0004260628,0.00001953932,0.0002771237,0.00001383903,0.0001812006,0.00005712306],"genre_scores_gemma":[0.9994156,0.00011539,0.000387278,0.00002373917,0.00001561722,0.00002970036,0.00000669262,0.000005516369,4.037034e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03196892,"threshold_uncertainty_score":0.2946821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00910449278242325,"score_gpt":0.2418185157194179,"score_spread":0.2327140229369946,"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."}}