{"id":"W4404770452","doi":"10.1016/j.isci.2024.111464","title":"Movi: A fast and cache-efficient full-text pangenome index","year":2024,"lang":"en","type":"article","venue":"iScience","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Johns Hopkins University; National Institutes of Health; National Science Foundation; National Human Genome Research Institute; National Institute of Health Sciences","keywords":"Index (typography); Cache; Computer science; Parallel computing; World Wide Web","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":[0.0006198496,0.001044385,0.001006515,0.00219445,0.000746462,0.001438935,0.002921582,0.0007953228,0.006321867],"category_scores_gemma":[0.002815242,0.0005703307,0.0006734081,0.003556209,0.0004866247,0.002788914,0.002289304,0.0009476683,0.003438481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009152291,"about_ca_system_score_gemma":0.00140518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003355044,"about_ca_topic_score_gemma":0.004610038,"domain_scores_codex":[0.9993444,0.00004764066,0.00005708952,0.0001552649,0.0003113092,0.00008426838],"domain_scores_gemma":[0.99914,0.0002255202,0.00008898185,0.0002327614,0.0002222224,0.00009049265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001880703,0.0002355582,0.003302296,0.0007239959,0.000162232,0.0003428411,0.0003344428,0.01203362,0.1290889,0.01216333,0.1124302,0.7273021],"study_design_scores_gemma":[0.0007204817,0.0009831804,0.006003599,0.0001144158,0.0001724422,0.00108319,0.0002951476,0.5364033,0.2723988,0.020122,0.1613774,0.0003261686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08985977,0.005001124,0.6916055,0.000861854,0.0006734683,0.0006034522,0.01737132,0.1811036,0.01291981],"genre_scores_gemma":[0.2281965,0.001413336,0.6982527,0.0005387469,0.0003532976,0.0008231039,0.05151744,0.0055013,0.01340364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006321867,"threshold_uncertainty_score":0.0211488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327211608512366,"score_gpt":0.2465614735406449,"score_spread":0.2332893574555212,"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."}}