{"id":"W2007761975","doi":"10.1016/j.chroma.2006.08.015","title":"Detection, characterization and identification of crucifer phytoalexins using high-performance liquid chromatography with diode array detection and electrospray ionization mass spectrometry","year":2006,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Genomics, phytochemicals, and oxidative stress","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Chemistry; Chromatography; Electrospray ionization; Mass spectrometry; Electrospray; High-performance liquid chromatography; Crucifer; Analytical Chemistry (journal); Direct electron ionization liquid chromatography–mass spectrometry interface; Fragmentation (computing); Ionization; Mass spectrum; Ion; Chemical ionization; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004219683,0.0006841922,0.0004489954,0.0006567211,0.0005648039,0.0005221852,0.0004077222,0.000579022,0.0006989627],"category_scores_gemma":[0.0005757638,0.000286728,0.0002707625,0.0005370841,0.0003607976,0.0006379797,0.0002596888,0.0008269516,0.0004496329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005475484,"about_ca_system_score_gemma":0.0007247871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002395436,"about_ca_topic_score_gemma":0.005226298,"domain_scores_codex":[0.9996336,0.00004159726,0.00003140594,0.0001031453,0.0001287958,0.00006138391],"domain_scores_gemma":[0.9994954,0.000151169,0.0000849376,0.0000493571,0.0001376086,0.0000815254],"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":[0.00006461956,0.00002128463,0.0003082977,0.00001889509,0.000004418809,0.00002016936,0.00001255357,0.00002350155,0.9973002,0.00004202116,0.00005073798,0.002133233],"study_design_scores_gemma":[0.00001812754,0.0001551159,0.00559972,0.000004319916,0.00001246326,0.0002844368,0.0000255525,0.0007230829,0.9914853,0.0001233451,0.001555875,0.00001280691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9099407,0.003563854,0.07970504,0.0005643856,0.0000648889,0.0002954571,0.00234141,0.001104099,0.002420138],"genre_scores_gemma":[0.9132228,0.002264879,0.07480726,0.0005087344,0.00004101964,0.0003236257,0.004525065,0.000181572,0.004125084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002395436,"threshold_uncertainty_score":0.004763007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003490793538257039,"score_gpt":0.1932910751541394,"score_spread":0.1898002816158824,"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."}}