{"id":"W2793054360","doi":"10.1093/gigascience/giy013","title":"10KP: A phylodiverse genome sequencing plan","year":2018,"lang":"en","type":"article","venue":"GigaScience","topic":"Plant Pathogens and Fungal Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":258,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia; Dalhousie University","funders":"Government of Jiangxi Province; Natural Environment Research Council; Sight Research UK","keywords":"Genome; Context (archaeology); Biology; Protist; Data science; DNA sequencing; Computational biology; Whole genome sequencing; Evolutionary biology; Computer science; Genetics; Gene","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":[0.005754157,0.001272455,0.0006319227,0.002103515,0.001047684,0.003696927,0.002786691,0.001916731,0.03537212],"category_scores_gemma":[0.005724818,0.000822923,0.0007624981,0.00359525,0.0006212611,0.003141093,0.003555905,0.003015599,0.0248366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001967684,"about_ca_system_score_gemma":0.00539802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008612917,"about_ca_topic_score_gemma":0.009900592,"domain_scores_codex":[0.9989645,0.0002786798,0.00007075656,0.0001891218,0.0003362716,0.0001606372],"domain_scores_gemma":[0.9978879,0.0003888663,0.0002182691,0.0003152636,0.0005841115,0.0006056516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001259176,0.0001324294,0.002883964,0.0008632226,0.00007883126,0.0003938813,0.0005923125,0.005017126,0.02065142,0.05915223,0.7905775,0.1183979],"study_design_scores_gemma":[0.0001507246,0.00008337238,0.002967724,0.0002204006,0.00002633128,0.0001358898,0.000221019,0.006199643,0.004049738,0.03013462,0.9557453,0.00006519866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.01237263,0.001949833,0.2423931,0.01600128,0.001404403,0.002789741,0.5151581,0.06906305,0.138868],"genre_scores_gemma":[0.01774462,0.001381067,0.4266592,0.002754196,0.0002022544,0.00307535,0.5224243,0.006907477,0.01885156],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03537212,"threshold_uncertainty_score":0.1183315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0200876518187081,"score_gpt":0.2270615127199526,"score_spread":0.2069738609012445,"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."}}