{"id":"W2955450066","doi":"10.1039/c9ay01137d","title":"An automated high-throughput sample preparation method using double-filtration for serum metabolite LC-MS analysis","year":2019,"lang":"en","type":"article","venue":"Analytical Methods","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Chromatography; Metabolite; Filtration (mathematics); Sample preparation; Sample (material); Throughput; Chemistry; Computer science; Biochemistry; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00226389,0.001943669,0.001721257,0.002673428,0.001039207,0.0008896624,0.001552757,0.001608907,0.001687622],"category_scores_gemma":[0.001788016,0.0009101258,0.0009058046,0.0008269921,0.0006150198,0.000843258,0.00130946,0.001729221,0.001962918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004749858,"about_ca_system_score_gemma":0.002194036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0011427,"about_ca_topic_score_gemma":0.002271743,"domain_scores_codex":[0.9971011,0.0003311748,0.0001826542,0.0006588515,0.001553167,0.000172886],"domain_scores_gemma":[0.9989464,0.0002063052,0.000147306,0.0001081584,0.0004549498,0.0001370271],"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":[0.000245476,0.0002334906,0.001081462,0.0002222504,0.00008965009,0.0001348572,0.00004166486,0.0001569281,0.9546282,0.0002739938,0.001587339,0.04130468],"study_design_scores_gemma":[0.0002549199,0.001294911,0.01153807,0.00004778725,0.0002338282,0.002637359,0.00004038676,0.01530486,0.9436186,0.0006110744,0.02412173,0.0002963559],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05440398,0.002440993,0.9326204,0.000317222,0.0003772022,0.002134271,0.001010677,0.005709285,0.0009859807],"genre_scores_gemma":[0.09818904,0.00161993,0.8890328,0.0008977447,0.000293304,0.004034586,0.002474971,0.0003184823,0.003139087],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002673428,"threshold_uncertainty_score":0.01197273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03789728691023356,"score_gpt":0.4546231306101219,"score_spread":0.4167258436998883,"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."}}