{"id":"W4402511613","doi":"10.1101/2024.09.08.611914","title":"Phylogeny-based selection of representative variants with Navargator: Proof of principle using humoral cross-reactivity data from two immunization studies","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Immunization; Proof of concept; Selection (genetic algorithm); Phylogenetics; Computational biology; Biology; Evolutionary biology; Computer science; Genetics; Machine learning; Antibody; Gene","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":[0.004483755,0.001042387,0.001199255,0.001367806,0.000894495,0.001282607,0.001160569,0.0009099624,0.002527389],"category_scores_gemma":[0.008963799,0.0006069271,0.001428531,0.001395089,0.0005352086,0.0006966737,0.001210976,0.001376286,0.001189461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005726811,"about_ca_system_score_gemma":0.0008933652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001100912,"about_ca_topic_score_gemma":0.001162589,"domain_scores_codex":[0.9977628,0.0007504968,0.0001543783,0.0007123959,0.0004538027,0.000166147],"domain_scores_gemma":[0.9961905,0.002101928,0.0004652877,0.000434825,0.0006042658,0.0002031534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003646663,0.00138053,0.1254204,0.002229574,0.00114646,0.0009674886,0.00106176,0.1193973,0.4533005,0.005914154,0.01538696,0.2701482],"study_design_scores_gemma":[0.0005223912,0.001889569,0.04411627,0.0001764993,0.000477724,0.0008877179,0.0003948733,0.7408506,0.1870517,0.007588898,0.01585549,0.0001883769],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5300415,0.0006496275,0.4500883,0.000336741,0.0001083834,0.0005213615,0.006441693,0.009345046,0.002467351],"genre_scores_gemma":[0.4656774,0.0002375867,0.5217829,0.0001435252,0.00002259208,0.0004914008,0.00978938,0.001333917,0.0005213085],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004483755,"threshold_uncertainty_score":0.02371269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1353034775619253,"score_gpt":0.4133723259025432,"score_spread":0.278068848340618,"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."}}