{"id":"W3132330856","doi":"10.1101/2021.02.16.431517","title":"AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Set (abstract data type); Reference genome; Source code; Code (set theory); Airlift; Indel; Data mining; Genome; Programming language; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004104571,0.003678133,0.002124972,0.0055101,0.002873024,0.002900968,0.003690924,0.002399808,0.03064496],"category_scores_gemma":[0.01512823,0.003208392,0.00334392,0.004707914,0.0008954535,0.003569963,0.004810563,0.004827747,0.03455391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009384879,"about_ca_system_score_gemma":0.00238313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005233965,"about_ca_topic_score_gemma":0.01364873,"domain_scores_codex":[0.9967126,0.0004710487,0.0003495316,0.00133236,0.00090342,0.0002310527],"domain_scores_gemma":[0.9954261,0.001790316,0.0004584889,0.001501211,0.0006601422,0.000163764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001657518,0.0002748302,0.007396223,0.00544511,0.001595265,0.001150487,0.002934194,0.007973714,0.1340273,0.007932878,0.5002927,0.3293198],"study_design_scores_gemma":[0.0006855814,0.0004250265,0.01372072,0.0009889923,0.0004694021,0.00262501,0.0007063319,0.07088084,0.1817176,0.02117378,0.7058206,0.0007861102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01725131,0.002293835,0.4875695,0.0004153107,0.0009167507,0.0005480504,0.09711261,0.386104,0.007788674],"genre_scores_gemma":[0.03276865,0.0006910341,0.7294124,0.0005037996,0.0001622822,0.001258066,0.1392012,0.08926427,0.00673821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03064496,"threshold_uncertainty_score":0.1025176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02439026645540882,"score_gpt":0.2411835329550993,"score_spread":0.2167932664996904,"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."}}