{"id":"W2883420742","doi":"10.1007/s00253-018-9218-8","title":"Imaging mass spectrometry-guided fast identification of antifungal secondary metabolites from Penicillium polonicum","year":2018,"lang":"en","type":"article","venue":"Applied Microbiology and Biotechnology","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Biotechnology Research Institute","funders":"Chinese Academy of Agricultural Sciences; Ministry of Agriculture of the People's Republic of China; Agricultural Science and Technology Innovation Program; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Penicillium; Antifungal; Biology; Drug discovery; Mass spectrometry imaging; Computational biology; Mass spectrometry; Fusarium; Identification (biology); Biochemistry; Microbiology; Chemistry; Chromatography; Botany","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":[0.0002540445,0.0002258166,0.0005515721,0.0003330861,0.000118642,0.00001116736,0.0001975284,0.0005426786,0.0003670747],"category_scores_gemma":[0.00007293712,0.0001765214,0.00007222371,0.0003399634,0.001421214,0.00003840879,0.000144143,0.0004005465,0.00006697896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002471628,"about_ca_system_score_gemma":0.00005142411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006280724,"about_ca_topic_score_gemma":0.00001113136,"domain_scores_codex":[0.998446,0.00003318583,0.0004947715,0.0006328096,0.00003656299,0.0003566766],"domain_scores_gemma":[0.9990929,0.00005364811,0.0002582327,0.0004467896,0.0001049066,0.00004349961],"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":[0.0001698519,0.00005325723,0.0006664487,0.00003009622,0.0001433726,0.000004352884,0.00003353681,5.999656e-9,0.9885225,0.002780741,0.001163024,0.00643286],"study_design_scores_gemma":[0.000705006,0.0001087319,0.003487417,0.00001546049,0.0001686093,0.0002119795,0.000103491,0.000002814787,0.9872913,0.0008641232,0.006863895,0.0001771232],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896145,0.002575475,0.0003797225,0.005923484,0.0002962172,0.0003345292,0.0002294058,0.0001196654,0.0005269977],"genre_scores_gemma":[0.9936891,0.000320089,0.004868589,0.0005354608,0.0002172701,0.000001603406,0.0001923873,0.00001737662,0.0001581707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006255737,"threshold_uncertainty_score":0.7198332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006708228470010935,"score_gpt":0.2254899611089529,"score_spread":0.218781732638942,"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."}}