{"id":"W2533446135","doi":"10.1093/nar/gkw980","title":"Exposome-Explorer: a manually-curated database on biomarkers of exposure to dietary and environmental factors","year":2016,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"FP7 Food, Agriculture and Fisheries, Biotechnology; European Commission; World Health Organization","keywords":"Exposome; Biomonitoring; Biorepository; Biology; Environmental epidemiology; Biomarker; Biobank; Environmental health; Data science; Database; Bioinformatics; Computer science; Medicine; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001490911,0.000281154,0.0002702596,0.0002465176,0.0002926848,0.00002697508,0.0005022965,0.0001271528,0.003629009],"category_scores_gemma":[0.0003366818,0.0002051554,0.00005677513,0.0003836907,0.001069724,0.0004785882,0.001061456,0.0003418,0.001232923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005498949,"about_ca_system_score_gemma":0.00002011453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001818752,"about_ca_topic_score_gemma":0.0000254696,"domain_scores_codex":[0.9956955,0.0005310346,0.0003991091,0.001051908,0.001413825,0.0009086533],"domain_scores_gemma":[0.9978725,0.0005602326,0.00007806918,0.0008446558,0.000008542404,0.0006360131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002752712,0.0002966433,0.2634357,0.00002288418,0.00003788107,0.00004058196,0.001658639,0.000008915373,0.6738948,0.00001140092,0.001712709,0.05860459],"study_design_scores_gemma":[0.001187167,0.001746801,0.9093145,0.0001632349,0.00001098579,0.000005846598,0.0034532,0.0000482,0.07688946,0.00009615769,0.006667139,0.0004172768],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958464,0.00004096696,0.00008352141,0.0009180671,0.00004560258,0.0008412263,0.0002992968,0.0000337339,0.001891168],"genre_scores_gemma":[0.9984058,0.0003726033,0.0004403117,0.0002121088,0.00002782387,0.00006353308,0.00004585215,0.00005583697,0.0003760876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6458789,"threshold_uncertainty_score":0.9995447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0638576445355641,"score_gpt":0.3251722743612152,"score_spread":0.2613146298256511,"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."}}