{"id":"W4404959820","doi":"10.3390/metabo14120671","title":"Comprehensive Blood Metabolome and Exposome Analysis, Annotation, and Interpretation in E-Waste Workers","year":2024,"lang":"en","type":"article","venue":"Metabolites","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fogarty International Center; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; International Development Research Centre; Genome Canada","keywords":"Exposome; Metabolome; Metabolomics; Biology; Physiology; Environmental health; Medicine; Bioinformatics","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":[0.0004113826,0.0001728823,0.0002892174,0.0002933282,0.0000683606,0.0001039763,0.00007099268,0.00005140946,0.0002206225],"category_scores_gemma":[0.00005398783,0.0001610757,0.00004845723,0.0009875948,0.0002059286,0.0005156739,0.0001210993,0.0001676024,0.00005902218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003509312,"about_ca_system_score_gemma":0.000007109329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002644554,"about_ca_topic_score_gemma":0.00009571287,"domain_scores_codex":[0.9984341,0.0001785885,0.0003015371,0.0006058093,0.0002127832,0.0002671738],"domain_scores_gemma":[0.9994401,0.0002254652,0.00005215774,0.0001628656,0.000006497731,0.000112899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002103394,0.0000594676,0.9049064,0.00007765997,0.0003855099,0.00003533592,0.006463727,0.001384314,0.03130741,0.0002783549,0.00002647877,0.05505433],"study_design_scores_gemma":[0.0002966116,0.0000249424,0.9763504,0.00003471574,0.0005448088,0.000006546039,0.001347127,0.01468138,0.002157194,0.0008171822,0.003521806,0.0002172482],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842767,0.01351609,0.001095997,0.0002729832,0.00006075574,0.0002530771,0.0000148037,0.0000391077,0.0004704844],"genre_scores_gemma":[0.9966863,0.001096251,0.001696152,0.0002981086,0.00002361269,0.00003922156,0.00002011347,0.00001554923,0.0001247161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07144406,"threshold_uncertainty_score":0.6568477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008961939382665422,"score_gpt":0.252772316108924,"score_spread":0.2438103767262586,"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."}}