{"id":"W4415548186","doi":"10.1021/acs.analchem.5c04612","title":"High-Frequency Microfluidic Fractionation for Compound-Resolved Bioactivity-Based Metabolomics","year":2025,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"thermodynamics and calorimetric analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"National Institute of General Medical Sciences; Office of Science; Joint Genome Institute; Simons Foundation; Deutsche Forschungsgemeinschaft; Science Foundation Ireland; National Institute of Diabetes and Digestive and Kidney Diseases; U.S. Department of Energy","keywords":"Metabolomics; Metabolome; Microfluidics; Fractionation; Metabolite; Bioanalysis; Mass spectrometry; Bioreporter","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001587605,0.0002479384,0.0003905346,0.00008944389,0.0001693664,0.00007477366,0.0003053525,0.0003055417,0.0007127736],"category_scores_gemma":[0.0003442732,0.0002521401,0.0003300924,0.0004870483,0.0001357802,0.00007054146,0.00004155773,0.0002837141,0.00001055421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002645612,"about_ca_system_score_gemma":0.0002308155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001317866,"about_ca_topic_score_gemma":0.000002430679,"domain_scores_codex":[0.9985843,0.000009882755,0.0003887227,0.0004724838,0.0002218705,0.0003227034],"domain_scores_gemma":[0.998592,0.0005116557,0.0001282853,0.000441551,0.0002164221,0.0001100485],"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":[0.0001253178,0.0003763437,0.001142779,0.0003326374,0.0005015407,0.000004797418,0.000002736719,0.00007275189,0.984046,0.009086767,0.002385028,0.001923316],"study_design_scores_gemma":[0.001671983,0.000009059448,0.0002011449,0.00004844493,0.0007369713,0.000002132324,0.00005821182,0.03602564,0.9299843,0.008334532,0.02248432,0.0004432051],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6643999,0.002217396,0.292271,0.002215805,0.00024595,0.0001819476,0.0004541445,0.0003085361,0.03770525],"genre_scores_gemma":[0.9940498,0.00005884069,0.001388495,0.0002565804,0.00017732,0.00003944775,0.0005331687,0.00002514521,0.003471153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3296499,"threshold_uncertainty_score":0.9999931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01410778940059756,"score_gpt":0.2726076726446685,"score_spread":0.2584998832440709,"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."}}