{"id":"W2778042508","doi":"10.1186/s12859-017-1953-9","title":"HISEA: HIerarchical SEed Aligner for PacBio data","year":2017,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Computational biology; DNA microarray; Biology; Genetics; Gene; Gene expression","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002458322,0.0001509873,0.0001566588,0.00001949336,0.0004195944,0.0001144801,0.0009358783,0.0001213209,0.000003193188],"category_scores_gemma":[0.0003035817,0.0001280334,0.00007792991,0.00001232074,0.0001516533,0.0000039624,0.0008816831,0.00004601594,0.00001555194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005841229,"about_ca_system_score_gemma":0.0000960763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007684977,"about_ca_topic_score_gemma":0.00005820311,"domain_scores_codex":[0.9991612,0.000009618339,0.0002582095,0.0002103248,0.0000938249,0.0002667656],"domain_scores_gemma":[0.9979702,0.00002142799,0.0001711019,0.001679986,0.00007116538,0.00008616017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001729431,0.0007375726,0.2329575,0.002332382,0.002169062,0.000006105551,0.002032882,0.0006463858,0.2394445,0.01191702,0.3782108,0.1278164],"study_design_scores_gemma":[0.002945724,0.0005306542,0.09176927,0.00003003581,0.0001466779,0.00002072699,0.0003255514,0.06431729,0.01176581,0.001242861,0.8260254,0.0008800174],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6376045,0.002770852,0.3121915,0.002359308,0.002892008,0.002960906,0.004651209,0.00004744472,0.03452231],"genre_scores_gemma":[0.617206,0.0004964167,0.3769622,0.0007909241,0.001092394,0.00006720496,0.001263053,0.000051309,0.002070439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4478146,"threshold_uncertainty_score":0.5221049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06171987483104954,"score_gpt":0.3073502706344804,"score_spread":0.2456303958034309,"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."}}