{"id":"W4280554122","doi":"10.12688/f1000research.110194.1","title":"The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms","year":2022,"lang":"en","type":"preprint","venue":"F1000Research","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Roche (Canada)","funders":"U.S. National Library of Medicine; NIHR Maudsley Biomedical Research Centre; Agricultural Research Service; Centers for Disease Control and Prevention; China Scholarship Council; Fonds Wetenschappelijk Onderzoek; Oxford Nanopore Technologies; Motor Neurone Disease Association; Large Facilities Office; National Institute for Health and Care Research; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Association of Public Health Laboratories; Norges Forskningsråd; U.S. Department of Agriculture; National Science Foundation","keywords":"Genotyping; Computational biology; Genomics; Data science; Bioinformatics; Biology; Medicine; Computer science; Genetics; Genome; Gene; Genotype","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":[0.03905386,0.003214586,0.001910565,0.009692132,0.003171549,0.01094808,0.004314364,0.005206448,0.1238539],"category_scores_gemma":[0.05561654,0.001782095,0.003421286,0.005339828,0.003676404,0.01293285,0.02646741,0.007987394,0.04423624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003301408,"about_ca_system_score_gemma":0.007413852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004916168,"about_ca_topic_score_gemma":0.005480153,"domain_scores_codex":[0.9841948,0.005081151,0.001003284,0.003222345,0.004860105,0.0016384],"domain_scores_gemma":[0.9349207,0.01898131,0.002287732,0.02365539,0.008762177,0.01139268],"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":[0.000257377,0.0002094533,0.002911763,0.0004967198,0.0001200775,0.0004264418,0.001337206,0.0009226881,0.004753076,0.02278644,0.7316767,0.2341021],"study_design_scores_gemma":[0.000076488,0.0001094775,0.002959463,0.0006427458,0.00003715656,0.000271579,0.0006355633,0.002028138,0.002069404,0.02832145,0.9627558,0.00009272408],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01250745,0.007429226,0.5095806,0.06236162,0.01920624,0.004180789,0.0483235,0.166097,0.1703137],"genre_scores_gemma":[0.05040729,0.005595401,0.5783004,0.0123264,0.003133019,0.007086382,0.107597,0.05322565,0.1823286],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1238539,"threshold_uncertainty_score":0.4143324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232577807820202,"score_gpt":0.3536213010709212,"score_spread":0.330363520288901,"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."}}