{"id":"W2071031086","doi":"10.1002/jsfa.2240","title":"The electronic nose as a tool for the classification of fruit and grape wines from different Ontario wineries","year":2005,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Nova Scotia Department of Agriculture; Agriculture and Agri-Food Canada","funders":"","keywords":"Wine; Electronic nose; Winery; Blowing a raspberry; Food science; Mathematics; Horticulture; Biology; Artificial intelligence; Computer science","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":[0.00006065312,0.00006693022,0.0001006804,0.00001207771,0.0001316909,0.00002828312,0.0003202336,0.00003272227,6.899706e-7],"category_scores_gemma":[0.00009174546,0.00002148105,0.00004878915,0.0001091141,0.0002824036,0.0001286348,0.00003280795,0.0001593325,2.353501e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004223353,"about_ca_system_score_gemma":0.00001056789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008526638,"about_ca_topic_score_gemma":0.0003418211,"domain_scores_codex":[0.9995129,0.000003422577,0.0001585227,0.00005416101,0.0001599747,0.0001110839],"domain_scores_gemma":[0.9995732,0.0001094897,0.0001371854,0.00009234762,0.0000728665,0.00001488657],"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":[0.00001054594,0.000007284794,0.00009851336,0.000004081089,0.0000198831,1.549646e-8,0.0001800425,0.0004769355,0.994459,0.0009801325,0.0001267761,0.003636768],"study_design_scores_gemma":[0.0001557302,0.0001292137,0.04900307,0.00003887139,0.00003506034,0.00001352307,0.0005798726,0.0002157793,0.9396629,0.008647752,0.001466613,0.00005164039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954004,0.001907467,0.00006281373,0.00246884,0.00005454357,0.00008249523,0.000003280698,0.000006865329,0.0000132533],"genre_scores_gemma":[0.999265,0.0004660447,0.0001652862,0.000006822547,0.00005308676,0.000001947121,1.079385e-7,0.000001817799,0.00003987828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05479614,"threshold_uncertainty_score":0.1040528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006343193168752806,"score_gpt":0.195384474258276,"score_spread":0.1890412810895231,"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."}}