{"id":"W2556805640","doi":"10.1016/j.cell.2016.11.007","title":"The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery","year":2016,"lang":"en","type":"article","venue":"Cell","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":563,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; BC Cancer Agency","funders":"Biotechnology and Biological Sciences Research Council; Medical Research Council; National Institute for Health and Care Research; British Heart Foundation; Cancer Research UK","keywords":"Biology; Epigenome; Blueprint; Scientific discovery; Computational biology; Data science; Genetics; DNA methylation; Gene; Computer science; Cognitive science; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.000232424,0.00004745581,0.00003265635,0.00001428728,0.0002063149,0.000148098,0.00009016778,0.00003532637,0.000003223909],"category_scores_gemma":[0.00005017187,0.0000291188,0.00002180491,0.00002221801,0.0001046357,0.00000468649,0.00005359845,0.00001106347,0.000002591577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009511987,"about_ca_system_score_gemma":0.00003755915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001233134,"about_ca_topic_score_gemma":0.00004809006,"domain_scores_codex":[0.9995631,0.00001359814,0.00009923279,0.0001800665,0.00005859548,0.00008540592],"domain_scores_gemma":[0.9996473,0.00002551068,0.00005140053,0.0001500173,0.0001016266,0.00002420558],"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":[0.00001510491,0.0000102121,0.001468646,0.000002484965,0.000005891386,5.659345e-8,0.00002049151,0.000002791242,0.9959559,0.000874882,0.0002535514,0.001389947],"study_design_scores_gemma":[0.0002879702,0.00005307761,0.002075073,0.000002442343,0.00000373606,1.00928e-7,0.00003270044,0.00001063718,0.8170387,0.001777294,0.1786662,0.00005205941],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897168,0.0007724654,0.007649399,0.0005755564,0.0003200621,0.0001597905,0.00003013799,0.000002884696,0.0007729094],"genre_scores_gemma":[0.9919441,0.0002215207,0.0001727484,0.00001911669,0.0001195694,0.00002552748,0.00004384284,0.000006278317,0.007447233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1789172,"threshold_uncertainty_score":0.1586828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01276755235793621,"score_gpt":0.2636279918611141,"score_spread":0.2508604395031779,"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."}}