{"id":"W1864672531","doi":"10.1111/j.1364-3703.2010.00643.x","title":"Mass spectrometry‐based metabolomics application to identify quantitative resistance‐related metabolites in barley against <i>Fusarium</i> head blight","year":2010,"lang":"en","type":"article","venue":"Molecular Plant Pathology","topic":"Mycotoxins in Agriculture and Food","field":"Agricultural and Biological Sciences","cited_by":171,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Clinical Research Institute; Grain Research Centre; Agriculture and Agri-Food Canada; McGill University; Ste. Anne's Hospital","funders":"Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Biology; Metabolomics; Fusarium; Gibberella zeae; Mycotoxin; Inoculation; Quantitative trait locus; Phenylpropanoid; Metabolome; Botany; Horticulture; Biochemistry; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003128212,0.0006989784,0.000473677,0.0008528682,0.0003130853,0.0004562041,0.0002086194,0.0004815251,0.001088448],"category_scores_gemma":[0.0002234611,0.0002376484,0.000566079,0.000771623,0.0001657582,0.0002745696,0.0002972143,0.0003626748,0.0003990079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003299391,"about_ca_system_score_gemma":0.0003534322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001688168,"about_ca_topic_score_gemma":0.002396626,"domain_scores_codex":[0.9998401,0.00001912402,0.00001016433,0.00006492464,0.00004032505,0.00002534465],"domain_scores_gemma":[0.9999162,0.00001450051,0.00002344688,0.000006365519,0.00002515053,0.00001432878],"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":[0.0002250447,0.00002940152,0.0009132437,0.0001262315,0.00003129056,0.00008570977,0.00002423529,0.0001033486,0.99247,0.00003813594,0.00005927093,0.005894214],"study_design_scores_gemma":[0.00004754789,0.000819344,0.06216557,0.00003579034,0.0001989984,0.001129924,0.0001412981,0.007695703,0.9217727,0.0002782524,0.005659591,0.00005522968],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8936216,0.008921483,0.08035102,0.0003194394,0.0001271151,0.0005890661,0.01006187,0.001101504,0.004907047],"genre_scores_gemma":[0.8662915,0.005249539,0.1184056,0.0003363985,0.00004297169,0.0004155663,0.003807395,0.000143371,0.005307616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001688168,"threshold_uncertainty_score":0.003641188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009296539619511346,"score_gpt":0.2463281644632669,"score_spread":0.2370316248437556,"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."}}