{"id":"W6888978933","doi":"10.24433/co.2530457.v2","title":"ProteinVAE: Variational Autoencoder for Design of Synthetic Viral Vector Serotypes","year":2023,"lang":"en","type":"other","venue":"Code Ocean","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoencoder; Vector (molecular biology); Pattern recognition (psychology); Code (set theory); Encoding (memory); Synthetic data","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":[0.0006741998,0.0009687716,0.0004407367,0.0002595118,0.0002410169,0.0004422307,0.0009338698,0.001134055,0.01172876],"category_scores_gemma":[0.002190894,0.0005227408,0.0005551576,0.0001974079,0.0003545891,0.000586738,0.0005368878,0.001151968,0.003941861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006384702,"about_ca_system_score_gemma":0.001216076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004531892,"about_ca_topic_score_gemma":0.007364274,"domain_scores_codex":[0.9997011,0.00006250172,0.00001319028,0.00005101991,0.0001318823,0.00004020832],"domain_scores_gemma":[0.9996139,0.0001739378,0.00002757085,0.00003412893,0.0001253712,0.00002518113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002465201,0.00009297855,0.0008988878,0.000290456,0.0000544347,0.00009952299,0.00005967893,0.7850381,0.02061035,0.03051314,0.06009388,0.1020021],"study_design_scores_gemma":[0.00001513842,0.00002380559,0.00005377539,0.000007815067,0.000002819755,0.00001079516,0.000004278115,0.9848098,0.006401996,0.002532577,0.00612965,0.00000761392],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0135714,0.0002548022,0.9607314,0.0002993252,0.0002594806,0.0001771807,0.003309137,0.008544765,0.01285241],"genre_scores_gemma":[0.2405967,0.0003152309,0.7224312,0.000421307,0.00006740249,0.0008082195,0.009029286,0.004046038,0.02228465],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01172876,"threshold_uncertainty_score":0.03923661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03883420164641642,"score_gpt":0.2745800423203548,"score_spread":0.2357458406739383,"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."}}