{"id":"W4290189723","doi":"10.1016/j.xgen.2022.100141","title":"Global priorities for large-scale biomarker-based prospective cohorts","year":2022,"lang":"en","type":"article","venue":"Cell Genomics","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Centre for Global Health Research","funders":"Medical Research Council; Cancer Research UK; Wellcome Trust","keywords":"Scale (ratio); Data collection; Data science; Diversity (politics); Data sharing; Environmental health; Computer science; Geography; Environmental resource management; Political science; Medicine; Sociology; Environmental science","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":[],"category_scores_codex":[0.0006109295,0.0001692128,0.000169382,0.00002298012,0.0006433235,0.00002559738,0.0002481376,0.00004558932,0.001222244],"category_scores_gemma":[0.00001793361,0.0002033246,0.00008729658,0.0001765523,0.0001105417,0.00007556334,0.0003640982,0.0001203015,0.0001198577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002252849,"about_ca_system_score_gemma":0.00007788539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007072489,"about_ca_topic_score_gemma":0.00008844953,"domain_scores_codex":[0.9982719,0.00009028411,0.0002200587,0.0006146883,0.0002700443,0.0005330362],"domain_scores_gemma":[0.9993541,0.00007611214,0.0001101807,0.0003178568,0.000005212889,0.0001365286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000259786,0.0006551226,0.9573928,0.00007359332,0.00002859271,0.00002609387,0.001776313,0.01188879,0.01340905,0.00009518486,0.006102251,0.008292404],"study_design_scores_gemma":[0.002910363,0.0004206684,0.4859738,0.000005709523,0.00006673969,0.00001415084,0.002244104,0.02320566,0.007055719,0.002380556,0.4749235,0.0007990045],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9634951,0.0001100555,0.01662944,0.0001914453,0.0003671332,0.001665783,0.0005064419,0.00006216125,0.01697239],"genre_scores_gemma":[0.9914195,0.00001465422,0.006072258,0.001155536,0.00005151215,0.0004195775,0.00005347035,0.00003507019,0.0007784287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.471419,"threshold_uncertainty_score":0.9996908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144408894097678,"score_gpt":0.2368029962014094,"score_spread":0.2253589072604326,"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."}}