{"id":"W2111164126","doi":"10.1093/nar/gkm967","title":"ORegAnno: an open-access community-driven resource for regulatory annotation","year":2007,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":241,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Vlaamse regering; Vetenskapsrådet; Fonds Wetenschappelijk Onderzoek; Genome British Columbia; Michael Smith Health Research BC; European Molecular Biology Laboratory; Canadian Institutes of Health Research; Genome Canada","keywords":"Ensembl; Annotation; Biology; Identifier; World Wide Web; Computational biology; RefSeq; Gene Annotation; DNA binding site; Computer science; Data curation; Information retrieval; Genome; Bioinformatics; Gene; Genomics; Genetics; Promoter","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":[0.003317054,0.0001288484,0.0001373257,0.0001244899,0.0007328729,0.0003768875,0.002118086,0.0002147798,0.0000218801],"category_scores_gemma":[0.0002173311,0.000134391,0.00004469052,0.0002291923,0.0002212616,0.00002736585,0.001415644,0.0003656189,0.000008729878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007251288,"about_ca_system_score_gemma":0.0001520326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001226088,"about_ca_topic_score_gemma":0.0004739239,"domain_scores_codex":[0.9983172,0.0003062703,0.0002483953,0.000336121,0.0002836065,0.0005084365],"domain_scores_gemma":[0.9982974,0.0000807958,0.00007019829,0.0009653821,0.0004089279,0.0001772903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000716313,0.0003685093,0.003552882,0.00008047267,0.00005402442,0.000004085095,0.0006402957,0.0002304082,0.9562804,0.00236972,0.01526667,0.02043622],"study_design_scores_gemma":[0.004652322,0.005388307,0.1535721,0.00008318482,0.00002788919,0.00003190846,0.006556017,0.008527255,0.197381,0.006812063,0.6158512,0.001116683],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895924,0.00005710296,0.002405426,0.0001643853,0.000049754,0.000628192,0.00004290219,0.00001584917,0.007044029],"genre_scores_gemma":[0.9933125,0.00002183788,0.0041092,0.0002036352,0.0002185634,0.00004401329,0.0006915613,0.00005861212,0.001340047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7588994,"threshold_uncertainty_score":0.563674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08204625031339052,"score_gpt":0.4179687881256982,"score_spread":0.3359225378123077,"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."}}