{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00128426,0.001323211,0.00074673,0.001427484,0.0005978631,0.001271575,0.001054386,0.0006265849,0.00150479],"category_scores_gemma":[0.001279635,0.0005966219,0.001318407,0.0007739835,0.0004841923,0.001059821,0.001618539,0.001112653,0.001365686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005239327,"about_ca_system_score_gemma":0.001115234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009601301,"about_ca_topic_score_gemma":0.001429773,"domain_scores_codex":[0.9988972,0.0001212495,0.000124718,0.0003872657,0.000354465,0.0001151373],"domain_scores_gemma":[0.9992105,0.0001368293,0.0001666972,0.0001916075,0.0002141737,0.00008023843],"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":[0.0002347647,0.00005930489,0.001264117,0.0002032345,0.000065961,0.0001545923,0.0001386949,0.0008768588,0.942509,0.0008980373,0.001178414,0.05241705],"study_design_scores_gemma":[0.00003131485,0.0001291486,0.00494004,0.00002606522,0.00004550626,0.0002598474,0.0000871561,0.02014268,0.9623127,0.001341788,0.01057478,0.0001088544],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1032621,0.0009043672,0.867071,0.0004826756,0.0001562738,0.0006071784,0.003292377,0.0220296,0.002194517],"genre_scores_gemma":[0.1276168,0.0007814137,0.8616531,0.0003333853,0.00005571021,0.001323411,0.004692837,0.001609453,0.001933891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00150479,"threshold_uncertainty_score":0.00679189,"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."}}