{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1032508,0.001764395,0.002554911,0.005242409,0.004670934,0.01075833,0.004591442,0.0108357,0.01367283],"category_scores_gemma":[0.06734287,0.0008634372,0.001456465,0.00618654,0.006306369,0.01030669,0.02928991,0.01347148,0.004015351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006225306,"about_ca_system_score_gemma":0.07754037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01235289,"about_ca_topic_score_gemma":0.0121716,"domain_scores_codex":[0.9695724,0.01455568,0.001515648,0.002549483,0.007705534,0.004101412],"domain_scores_gemma":[0.8734782,0.0212006,0.004316947,0.01958315,0.02260927,0.05881181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005549509,0.0002470773,0.005529764,0.0007681719,0.0003197563,0.000297931,0.001396394,0.001022993,0.002994011,0.2195319,0.5732612,0.1940759],"study_design_scores_gemma":[0.0001621181,0.0001151504,0.005316722,0.00081808,0.00006028512,0.0001439099,0.001121967,0.000526757,0.001338862,0.1284163,0.8618835,0.00009628308],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.00796489,0.03207189,0.06016853,0.8281204,0.03117521,0.0007078611,0.007196027,0.001977189,0.03061809],"genre_scores_gemma":[0.1969068,0.03457338,0.3832648,0.2702194,0.02076295,0.005004143,0.02970513,0.002611543,0.05695176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1032508,"threshold_uncertainty_score":0.5460488,"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."}}