{"id":"W4323660530","doi":"10.21203/rs.3.rs-2652649/v1","title":"Optimization of fecal sample homogenization for untargeted metabolomics","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Genome Alberta; DNA Genotek; Genome Canada","keywords":"Homogenization (climate); Reproducibility; Feces; Sample preparation; Chromatography; Materials science; Chemistry; Biological system; Biology; Microbiology","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":[0.002197795,0.0007799913,0.0007077826,0.0007280872,0.0003402567,0.001174107,0.0007104937,0.0007221063,0.00419295],"category_scores_gemma":[0.002433119,0.0002938834,0.0006715014,0.0006250824,0.0003940015,0.0007041362,0.0006795822,0.0008483423,0.00258425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004843585,"about_ca_system_score_gemma":0.0009743775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006303834,"about_ca_topic_score_gemma":0.001735848,"domain_scores_codex":[0.9987154,0.0003593193,0.0001413606,0.0003072319,0.0003550938,0.0001216215],"domain_scores_gemma":[0.9989914,0.00031538,0.0002061019,0.0001069897,0.0003155313,0.00006451079],"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":[0.0003466978,0.0001043986,0.001011062,0.001343077,0.00005544895,0.0001321067,0.00006652072,0.0005481187,0.9684545,0.0004521106,0.002098357,0.02538759],"study_design_scores_gemma":[0.0000407665,0.0007293674,0.007304491,0.0002509445,0.0001187965,0.0002034588,0.00008792523,0.003065109,0.9530125,0.0004890536,0.034631,0.00006644481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5227188,0.03930483,0.3937742,0.004548854,0.00318972,0.002910675,0.01147241,0.003483905,0.01859657],"genre_scores_gemma":[0.5716764,0.02627041,0.3720457,0.002555372,0.0005358591,0.002256041,0.01325174,0.0009867544,0.0104217],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00419295,"threshold_uncertainty_score":0.01402682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08825569311389672,"score_gpt":0.4151462481352496,"score_spread":0.3268905550213529,"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."}}