{"id":"W3040813543","doi":"10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a4227","title":"Coupling Chemometrics and Metabolomics Technologies to Identify a Molecular Fingerprint for ARDS","year":2020,"lang":"en","type":"article","venue":"","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Chemometrics; Fingerprint (computing); Computer science; Coupling (piping); Metabolomics; Fingerprint recognition; ARDS; Data mining; Computational biology; Artificial intelligence; Chemistry; Materials science; Machine learning; Medicine; Chromatography; Biology; Internal medicine","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":[0.002222816,0.0009652725,0.001155662,0.004661778,0.0003889514,0.001576894,0.00069235,0.0008164603,0.001141815],"category_scores_gemma":[0.003228506,0.0003197159,0.0005849238,0.003505253,0.0004294483,0.001084225,0.0008651204,0.001163729,0.0003490821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005463616,"about_ca_system_score_gemma":0.0007093712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001388724,"about_ca_topic_score_gemma":0.001620861,"domain_scores_codex":[0.9988747,0.0002364664,0.00007436595,0.0002084895,0.0005278471,0.00007806041],"domain_scores_gemma":[0.9989183,0.0003234612,0.0002632077,0.00009570865,0.0003296079,0.0000698229],"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":[0.0006163407,0.0006217389,0.09207456,0.0006357738,0.0005009816,0.0002590227,0.0001416613,0.004425865,0.6797789,0.002006833,0.00113068,0.2178076],"study_design_scores_gemma":[0.00008436909,0.001342669,0.3082175,0.0001486067,0.0006000352,0.003129493,0.0005082032,0.1693007,0.496609,0.01095754,0.008888916,0.0002129476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5530407,0.01184449,0.4213812,0.002088407,0.0003583503,0.0005172135,0.002823706,0.001556153,0.006389726],"genre_scores_gemma":[0.7698277,0.003686157,0.2230628,0.0006405335,0.0002619227,0.0002295792,0.0009324459,0.0001062552,0.001252677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004661778,"threshold_uncertainty_score":0.01175553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141822886212547,"score_gpt":0.2954726558781918,"score_spread":0.2740544270160663,"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."}}