{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001885273,0.001405761,0.0009025813,0.001777513,0.0006563897,0.001688285,0.002417554,0.001262664,0.003948838],"category_scores_gemma":[0.004843407,0.000882772,0.001567225,0.001195634,0.0007068898,0.001741226,0.001847387,0.002786927,0.002505162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009568353,"about_ca_system_score_gemma":0.001221562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002660227,"about_ca_topic_score_gemma":0.005545642,"domain_scores_codex":[0.9992556,0.0001426949,0.00004561506,0.0002568368,0.0002574042,0.00004185051],"domain_scores_gemma":[0.9980416,0.001039436,0.0001846134,0.0003196185,0.0003226255,0.00009201266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006529452,0.0002780011,0.004195033,0.001085591,0.0005878914,0.0003721901,0.0005705235,0.318172,0.09713861,0.0362596,0.02532295,0.5153647],"study_design_scores_gemma":[0.00003734762,0.00004229969,0.0005515987,0.00002510901,0.00002195434,0.0001033574,0.00004358873,0.9388914,0.0282556,0.01764937,0.0143139,0.00006439755],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004140023,0.00009696034,0.977555,0.0000580553,0.00004563053,0.00004032698,0.0006301954,0.01692591,0.0005078014],"genre_scores_gemma":[0.03549176,0.0001189626,0.9574716,0.0001022058,0.00002064133,0.0001300873,0.002927264,0.00259177,0.001145815],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003948838,"threshold_uncertainty_score":0.01321018,"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."}}