{"id":"W2945962433","doi":"10.1101/gr.244830.118","title":"Recompleting the <i>Caenorhabditis elegans</i> genome","year":2019,"lang":"en","type":"article","venue":"Genome Research","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":169,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Core Research for Evolutional Science and Technology; National Institute of General Medical Sciences; National Institutes of Health; National Institute of Food and Agriculture; Japan Science and Technology Corporation; National Institute of Allergy and Infectious Diseases; Japan Agency for Medical Research and Development; Gordon and Betty Moore Foundation; Michigan State University; College of Engineering, Michigan State University; U.S. Department of Agriculture; National Science Foundation","keywords":"Biology; Caenorhabditis elegans; Genome; Genetics; Gene; Caenorhabditis; Genomics; Whole genome sequencing; Sequence assembly; Multicellular organism; Computational biology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001875451,0.0001864178,0.0001806656,0.00009336502,0.0003991231,0.000134577,0.0009230022,0.0001586831,0.0005278174],"category_scores_gemma":[0.0001008144,0.0001521677,0.0001122337,0.0002517918,0.0002147754,0.000004506535,0.0005657603,0.0004668602,0.001244416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004587482,"about_ca_system_score_gemma":0.0001552844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001097794,"about_ca_topic_score_gemma":0.00005566856,"domain_scores_codex":[0.997395,0.0003398301,0.0002564327,0.000598405,0.0005435822,0.0008667521],"domain_scores_gemma":[0.99839,0.00006316453,0.00005603982,0.001061201,0.0002838093,0.0001458239],"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":[0.000026584,0.00003986494,0.001802857,0.00003971041,0.00005119455,0.000004919054,0.0005105258,0.0003023716,0.994505,0.0001369438,0.00162464,0.0009553565],"study_design_scores_gemma":[0.0005416393,0.0005601816,0.02039249,0.00001041229,0.00001160237,0.00005489828,0.0006463541,0.00009096495,0.03986749,0.0005255921,0.9368854,0.0004129418],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9807063,0.00273997,0.0009474515,0.001449515,0.0002537479,0.0006690014,0.00003509759,0.00002307242,0.01317586],"genre_scores_gemma":[0.9873359,0.0008016342,0.0006289437,0.0004602913,0.000830033,0.00003710942,0.0001348448,0.00006138946,0.009709872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9546375,"threshold_uncertainty_score":0.9995332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03355998618102909,"score_gpt":0.3037409373668502,"score_spread":0.2701809511858211,"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."}}