{"id":"W4403595414","doi":"10.1021/acsmeasuresciau.4c00035","title":"Rapid and Robust Workflows Using Different Ionization, Computation, and Visualization Approaches for Spatial Metabolome Profiling of Microbial Natural Products in <i>Pseudoalteromonas</i>","year":2024,"lang":"en","type":"article","venue":"ACS Measurement Science Au","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Metabolome; Metabolomics; Visualization; Hyperspectral imaging; Profiling (computer programming); Microbiome; Computer science; Artificial intelligence; Computational biology; Pattern recognition (psychology); Biology; Bioinformatics","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":[0.001085192,0.0001231671,0.0001619127,0.0001654968,0.0001417372,0.00006949779,0.00007950589,0.00003435624,2.668287e-7],"category_scores_gemma":[0.0002887066,0.0001032197,0.00001872035,0.0003804437,0.0001987652,0.00002341856,0.00008773382,0.00003812309,3.729418e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005297593,"about_ca_system_score_gemma":0.0001832023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001989306,"about_ca_topic_score_gemma":0.000060477,"domain_scores_codex":[0.9988145,0.00003472305,0.0002367732,0.0004370471,0.0002821926,0.0001947593],"domain_scores_gemma":[0.9994846,0.000008894176,0.00007508212,0.00008665689,0.000316573,0.00002822402],"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":[0.00002447322,0.00003628297,0.0028899,0.0001381613,0.00002535072,1.114873e-7,0.0001699758,0.0006754573,0.9927348,0.0005226809,0.00001237627,0.002770396],"study_design_scores_gemma":[0.000427095,0.0001030969,0.007919982,0.00006158435,0.00004601401,0.000004756593,0.0001111219,0.03214907,0.9587772,0.000131762,0.0001024362,0.0001659103],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9333948,0.003973227,0.06157951,0.0001081805,0.0003390685,0.0005823641,0.000007502286,0.000006306518,0.00000901906],"genre_scores_gemma":[0.9930387,0.0001854368,0.006582226,0.0000168016,0.0001111026,0.00002315657,0.00002847595,0.000009979371,0.000004094865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05964391,"threshold_uncertainty_score":0.4209179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0681637689344575,"score_gpt":0.2732368019551596,"score_spread":0.2050730330207021,"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."}}