{"id":"W2281404194","doi":"10.1039/c6ra01192f","title":"High-resolution mass spectrometry for exploring metabolic signatures of sepsis-induced acute kidney injury","year":2016,"lang":"en","type":"article","venue":"RSC Advances","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Burnaby Hospital; Simon Fraser University","funders":"","keywords":"Acute kidney injury; Mass spectrometry; Sepsis; Resolution (logic); Chemistry; Metabolomics; Medicine; Internal medicine; Chromatography; Computer science; Artificial intelligence","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":[0.0009541779,0.0007025606,0.0004132015,0.001844843,0.0004206879,0.0007398317,0.0004824616,0.001013784,0.001271268],"category_scores_gemma":[0.001087794,0.0002466633,0.0003368841,0.001172281,0.0002768507,0.0005989645,0.0007088319,0.001037394,0.0006115218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002306888,"about_ca_system_score_gemma":0.0004106271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006170513,"about_ca_topic_score_gemma":0.001181943,"domain_scores_codex":[0.9995531,0.0001331856,0.00002557885,0.000086333,0.0001576382,0.00004406515],"domain_scores_gemma":[0.999645,0.00008936768,0.00009811592,0.00002850803,0.00008735369,0.00005171485],"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":[0.0006514722,0.0001925535,0.01758383,0.0006131302,0.0003610964,0.0006961026,0.00008607043,0.0006778097,0.9361714,0.00105029,0.001733877,0.0401823],"study_design_scores_gemma":[0.0001557621,0.00114794,0.1044733,0.0003014204,0.0006245248,0.007461863,0.0004173589,0.03910041,0.8173825,0.005365414,0.02336813,0.000201368],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7914083,0.07086681,0.1152509,0.003124838,0.0006661107,0.0003896447,0.004964782,0.001600336,0.01172814],"genre_scores_gemma":[0.8812183,0.01914866,0.09253328,0.001833133,0.0003416402,0.0002096434,0.001625359,0.0001530911,0.002936883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001844843,"threshold_uncertainty_score":0.005046248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01750728598796089,"score_gpt":0.2736507563851648,"score_spread":0.2561434703972039,"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."}}