{"id":"W4414542273","doi":"10.1021/jasms.5c00193","title":"Rapid Screening and Prioritization of Culture Conditions for Natural Product Discovery using the Liquid Microjunction Surface Sampling Probe","year":2025,"lang":"en","type":"article","venue":"Journal of the American Society for Mass Spectrometry","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Drug discovery; Sampling (signal processing); Natural product; Prioritization; Workflow; Mass spectrometry; Pipeline (software); Sample preparation","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":[0.000528249,0.0005368558,0.0004686505,0.0003739482,0.0002934179,0.0009771264,0.0003922261,0.0004045179,0.0007198883],"category_scores_gemma":[0.0008043097,0.0002285472,0.0003991967,0.0003805528,0.0003261076,0.0005532852,0.0006740405,0.0007913309,0.0003776244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003025491,"about_ca_system_score_gemma":0.0005439646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005563423,"about_ca_topic_score_gemma":0.001873072,"domain_scores_codex":[0.9995579,0.00005956327,0.00002477847,0.0001085499,0.0002014244,0.00004784875],"domain_scores_gemma":[0.9996082,0.0001611879,0.00008118103,0.0000360127,0.00008815816,0.00002524514],"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":[0.00004611532,0.00002648198,0.0006111683,0.00002649317,0.000004306572,0.00002286003,0.00001351306,0.0004072753,0.9926242,0.00005234643,0.00003877197,0.006126368],"study_design_scores_gemma":[0.000007097209,0.0001644359,0.00241682,0.000004296169,0.000009976878,0.00004757928,0.00005023529,0.009143545,0.987439,0.0000656455,0.0006393074,0.00001209065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9235823,0.0005650579,0.07279405,0.0002266458,0.00004709716,0.0001330679,0.0005129935,0.0005935297,0.001545194],"genre_scores_gemma":[0.8859716,0.0005327592,0.1111257,0.0001610732,0.00001842213,0.0001647185,0.0005937151,0.0001313899,0.001300672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009771264,"threshold_uncertainty_score":0.00279367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047043606670104,"score_gpt":0.3113160932573192,"score_spread":0.2908456571906182,"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."}}