{"id":"W4386220340","doi":"10.1186/s13059-023-03033-5","title":"Ariadne: synthetic long read deconvolution using assembly graphs","year":2023,"lang":"en","type":"article","venue":"Genome biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Biology; Deconvolution; Computational biology; Linkage (software); Fragment (logic); Computer science; Genetics; Algorithm; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002549802,0.0001830757,0.0002001099,0.0001141516,0.0001693342,0.00001298998,0.0002046004,0.00021375,0.00002088668],"category_scores_gemma":[0.0000581862,0.0001779522,0.0001118118,0.0001978637,0.0001355878,6.403011e-7,0.0002301908,0.00006319967,0.00008562281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001972599,"about_ca_system_score_gemma":0.0000589699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004634326,"about_ca_topic_score_gemma":0.00005106603,"domain_scores_codex":[0.998726,0.00008332519,0.0002266675,0.0004692715,0.00004749086,0.0004472052],"domain_scores_gemma":[0.9993933,0.00002019177,0.00008570489,0.0003633459,0.00006516423,0.00007226669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002283241,0.00001461307,0.0116711,0.000008829298,0.0001035722,0.000003579391,0.00003325747,0.0001827439,0.9859397,0.0004107801,0.0001167115,0.001492243],"study_design_scores_gemma":[0.001960234,0.001526636,0.7018942,0.00002869171,0.0002393727,0.0002268446,0.0004157321,0.0009287019,0.0809316,0.01013955,0.1997837,0.001924756],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950679,0.002638415,0.0007917274,0.0001002951,0.0005435238,0.0001652257,0.00004907391,0.00001980419,0.000624037],"genre_scores_gemma":[0.9974883,0.001062097,0.0004671629,0.0001255135,0.0002624748,0.00002248438,0.0001851065,0.00002751578,0.0003594043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9050081,"threshold_uncertainty_score":0.7256678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0263379777492649,"score_gpt":0.2754917186387557,"score_spread":0.2491537408894908,"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."}}