{"id":"W3215418111","doi":"10.1021/acs.analchem.1c02660","title":"An Introduction to the Benchmarking and Publications for Non-Targeted Analysis Working Group","year":2021,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute of Food and Agriculture; National Institute of Standards and Technology","keywords":"Benchmarking; Harmonization; Consistency (knowledge bases); Data science; Variety (cybernetics); Dissemination; Standardization; Set (abstract data type); Chemistry; Computer science; Management science; Engineering; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002318371,0.0001062238,0.0001632643,0.00003435141,0.0001840547,0.00008445061,0.0001251415,0.00007656644,0.00005456979],"category_scores_gemma":[0.0002652738,0.00008777768,0.0001148405,0.0005642748,0.00004294558,0.00000363154,0.0000985728,0.00007122509,5.306538e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001142238,"about_ca_system_score_gemma":0.00002453366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003349322,"about_ca_topic_score_gemma":0.00004384626,"domain_scores_codex":[0.9990714,0.00001538764,0.0001592015,0.0004713475,0.00008430565,0.0001984056],"domain_scores_gemma":[0.9992542,0.00002340265,0.00004112766,0.0004382211,0.0001372272,0.0001058637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002880089,0.00008889448,0.008987144,0.00001938346,0.00101401,6.146129e-7,0.00002954578,0.0001396888,0.9795937,0.001290691,0.006628321,0.00217919],"study_design_scores_gemma":[0.0005076685,0.0001469118,0.03336917,0.000005184878,0.001678193,0.00001614221,0.0005415578,0.01644916,0.3872604,0.0003260676,0.5591382,0.0005613804],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9183241,0.0007048456,0.06545898,0.01380928,0.0001100655,0.000177923,0.0000388973,0.00001748972,0.001358423],"genre_scores_gemma":[0.9930167,0.0000935126,0.003592698,0.00029708,0.00141698,0.00004339822,0.0005688139,0.000009558637,0.0009613177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5923333,"threshold_uncertainty_score":0.357947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01049055826549888,"score_gpt":0.2672746764254285,"score_spread":0.2567841181599296,"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."}}