{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005376495,0.0004411016,0.0005978616,0.0004479324,0.00005843642,0.00003146602,0.0004787948,0.0004184423,0.001531904],"category_scores_gemma":[0.0005942186,0.0004336236,0.0001981785,0.0002881455,0.0001408172,0.00005605289,0.00007046411,0.0001803175,0.002552295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001606816,"about_ca_system_score_gemma":0.0004167577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004628298,"about_ca_topic_score_gemma":0.0001028296,"domain_scores_codex":[0.9978667,0.00016271,0.0004089476,0.0006046344,0.0005173378,0.0004397009],"domain_scores_gemma":[0.9982774,0.0003255112,0.0005526785,0.0006031357,0.0001452716,0.00009600698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000189201,0.000149468,0.00006994887,0.0005591423,0.0004481527,0.000006290578,0.0001200339,0.002134874,0.0006980889,0.008442423,0.9870332,0.000149211],"study_design_scores_gemma":[0.005380967,0.001175959,0.001149655,0.004526607,0.001460876,0.00002419358,0.00004766318,0.2066113,0.003526091,0.0209817,0.7515225,0.003592463],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00006378187,0.001827083,0.763013,0.0007645821,0.00430505,0.03116233,0.03492274,0.01530009,0.1486413],"genre_scores_gemma":[0.001841563,0.0000176065,0.1027627,0.00004490431,0.001132203,0.0006293591,0.0004081335,0.01074295,0.8824206],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7337792,"threshold_uncertainty_score":0.9998115,"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."}}