{"id":"W1533146036","doi":"10.1186/s12864-015-1647-5","title":"An assembly and alignment-free method of phylogeny reconstruction from next-generation sequencing data","year":2015,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":186,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Institute of Botany, Chinese Academy of Sciences; Kunming Institute of Botany, Chinese Academy of Sciences; Beijing Institute of Genomics, Chinese Academy of Sciences; Chinese Academy of Sciences; National Science Foundation","keywords":"Genome; Biology; Phylogenetic tree; Phylogenomics; Alignment-free sequence analysis; Phylogenetics; Computational biology; Sequence assembly; Genomics; Whole genome sequencing; DNA sequencing; Multiple sequence alignment; Hybrid genome assembly; Evolutionary biology; Pairwise comparison; Comparative genomics; Bootstrapping (finance); Reference genome; Genetics; Sequence alignment; Computer science; Gene; Clade; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0003880951,0.000135158,0.0001680607,0.00002672193,0.0000591484,0.00003505423,0.0003634295,0.0001096758,0.000001688626],"category_scores_gemma":[0.0000988547,0.0001412721,0.00002664256,0.00003224426,0.00005252388,0.000005075752,0.0003812102,0.00003620992,8.663063e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002794952,"about_ca_system_score_gemma":0.0002189812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003771332,"about_ca_topic_score_gemma":0.0008266864,"domain_scores_codex":[0.998989,0.0001016088,0.0002464728,0.000443575,0.00008140797,0.0001378627],"domain_scores_gemma":[0.9986647,0.00001462549,0.0001386678,0.0009911066,0.00009548935,0.00009538996],"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":[0.00003008907,0.00001314067,0.004170507,0.000005161182,0.00005676581,2.171617e-7,0.000172421,0.0006025718,0.9856767,0.00004003728,0.0002766671,0.008955706],"study_design_scores_gemma":[0.001126924,0.0003322743,0.003288859,0.000005851648,0.0001129719,0.00003368172,0.001821058,0.02534167,0.962458,0.001855371,0.003272394,0.0003509175],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9684853,0.001979962,0.02869625,0.00002242907,0.0002594562,0.000133137,0.0002988756,0.000002960357,0.0001216602],"genre_scores_gemma":[0.7918678,0.0003224674,0.2068948,0.00005370907,0.0004423789,0.000004787591,0.0003818408,0.00001735002,0.00001486669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1781986,"threshold_uncertainty_score":0.5760911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.142870198343256,"score_gpt":0.3041024578783139,"score_spread":0.1612322595350578,"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."}}