{"id":"W4386745241","doi":"10.1101/2023.09.10.557072","title":"The <i>tidyomics</i> ecosystem: Enhancing omic data analyses","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Health and Medical Research Council; Medical Research Council","keywords":"Ecosystem; Omics; Biology; Computer science; Computational biology; Data science; Ecology; Bioinformatics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001208301,0.0006171782,0.0005392507,0.0001143415,0.0004445986,0.000434314,0.002391633,0.0006873231,0.000005106349],"category_scores_gemma":[0.0004146785,0.0005411183,0.0002405921,0.0002799513,0.0001285369,0.00001297005,0.001838851,0.000633295,0.00008933422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009382214,"about_ca_system_score_gemma":0.0007524189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001539305,"about_ca_topic_score_gemma":0.0003611593,"domain_scores_codex":[0.996403,0.000215454,0.0007617325,0.0015719,0.0003372068,0.0007106987],"domain_scores_gemma":[0.9948237,0.00008941679,0.0004364876,0.004125282,0.0003062823,0.000218854],"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":[0.00004649719,0.00004671528,0.0017277,0.0002036956,0.0003790912,0.0000146259,0.000002690153,0.000186285,0.9940338,0.00002775721,0.003328753,0.000002444888],"study_design_scores_gemma":[0.0004381665,0.00005939204,0.003498242,0.0002520302,0.0002566847,3.849839e-8,0.000007029038,0.001450265,0.9436145,0.000002302258,0.04953261,0.000888764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980828,0.004147647,0.007324212,0.0004168966,0.004490483,0.0007707141,0.001713426,0.0002957468,0.0000129064],"genre_scores_gemma":[0.9935817,0.003155167,0.001041064,0.0002269633,0.001608921,0.00009401633,0.00001960006,0.0002317536,0.00004087358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05041927,"threshold_uncertainty_score":0.9997041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05396177458496518,"score_gpt":0.275607281684813,"score_spread":0.2216455070998478,"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."}}