{"id":"W1990744911","doi":"10.1016/j.chroma.2012.12.043","title":"Automated determination of phenolic compounds in wine, berry, and grape samples using 96-blade solid phase microextraction system coupled with liquid chromatography–tandem mass spectrometry","year":2013,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Fermentation and Sensory Analysis","field":"Agricultural and Biological Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chemistry; Chromatography; Wine; Grape wine; Mass spectrometry; Solid-phase microextraction; Tandem mass spectrometry; Liquid chromatography–mass spectrometry; Berry; Gas chromatography–mass spectrometry","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":[],"consensus_categories":[],"category_scores_codex":[0.0002793814,0.0002061367,0.0005425396,0.0004708678,0.0001374601,0.0001052124,0.0001424366,0.0001317803,0.00006442287],"category_scores_gemma":[0.00001092385,0.00009703436,0.0002313873,0.001680764,0.0001411847,0.0005111871,0.00001163748,0.0001881879,6.742826e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004442511,"about_ca_system_score_gemma":0.0000158965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002552374,"about_ca_topic_score_gemma":0.00009183229,"domain_scores_codex":[0.998255,0.0001508952,0.0007960727,0.0001922009,0.000368957,0.0002368692],"domain_scores_gemma":[0.9984686,0.0001281978,0.0009644002,0.00005715392,0.0002536818,0.0001279202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001535848,0.0002531067,0.02391997,0.00005705342,0.000137505,0.00001676791,0.0001419854,0.00002825805,0.9748898,0.00001416461,0.00001816939,0.0003696718],"study_design_scores_gemma":[0.005425555,0.004663782,0.7872778,0.001059189,0.0005838485,0.00166671,0.008401562,0.06895728,0.1210031,0.0001744974,0.00005587714,0.0007308236],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987845,0.0002279371,0.0004933689,0.0001318023,0.00004782521,0.0002130091,0.00001053219,0.00005318293,0.00003778888],"genre_scores_gemma":[0.9970382,0.00008955345,0.002744642,0.00003056183,0.00007352111,0.000004092384,0.00001514763,0.000003147168,0.000001101133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8538867,"threshold_uncertainty_score":0.3956946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01818527025072941,"score_gpt":0.2676709457271885,"score_spread":0.2494856754764591,"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."}}