{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002093419,0.002736063,0.001761844,0.003434693,0.001429307,0.001995671,0.003415332,0.001562802,0.06347994],"category_scores_gemma":[0.004424469,0.001490997,0.001406834,0.003799666,0.0005753718,0.001981435,0.001557261,0.004266134,0.04566369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009237772,"about_ca_system_score_gemma":0.00206232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002621613,"about_ca_topic_score_gemma":0.003643087,"domain_scores_codex":[0.9983848,0.0002531342,0.000159007,0.0005508019,0.0005298632,0.000122427],"domain_scores_gemma":[0.9990842,0.0002618683,0.0001567865,0.0001865769,0.0002253524,0.00008521448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00175938,0.0002375974,0.002426321,0.002348,0.0004820672,0.0007947212,0.0006469084,0.005332144,0.06682827,0.009617068,0.5816383,0.3278893],"study_design_scores_gemma":[0.0006233283,0.0002212824,0.004289443,0.0002632836,0.0001410194,0.0009434371,0.0001822354,0.06510735,0.1214884,0.01689526,0.7895662,0.0002789155],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005323015,0.001060978,0.5124958,0.0004154682,0.0004583883,0.0009488353,0.0790982,0.3909017,0.009297748],"genre_scores_gemma":[0.01026801,0.0004042031,0.8505724,0.0004975699,0.00007987796,0.001511839,0.09880903,0.03215288,0.005704246],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06347994,"threshold_uncertainty_score":0.2123615,"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."}}