{"id":"W4386985988","doi":"10.1101/2023.09.20.558648","title":"GPFN: Prior-Data Fitted Networks for Genomic Prediction","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Inference; Machine learning; Computer science; Genomic selection; Artificial intelligence; Artificial neural network; Best linear unbiased prediction; Bayesian probability; Task (project management); Selection (genetic algorithm); Data mining; Biology; Genetics; Engineering; Genotype","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":[0.001306706,0.0009534518,0.0006192692,0.0006127543,0.0003173953,0.0006221863,0.00147167,0.001347245,0.002902455],"category_scores_gemma":[0.005451399,0.0004748763,0.0006208589,0.0005908971,0.0006290402,0.001134215,0.0007517561,0.00215957,0.0007254404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009038498,"about_ca_system_score_gemma":0.0007733521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01412295,"about_ca_topic_score_gemma":0.01280648,"domain_scores_codex":[0.999673,0.0001078524,0.00001046878,0.0001096053,0.00006502613,0.00003395006],"domain_scores_gemma":[0.9985021,0.001026292,0.00008131601,0.000129748,0.0002100189,0.00005059958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001097813,0.00006186325,0.003175724,0.00004721152,0.00006822079,0.00009440219,0.00003842536,0.8955756,0.001603407,0.005559967,0.006368564,0.0872968],"study_design_scores_gemma":[0.000004993764,0.000006674039,0.0001537584,0.00000474609,0.000003525394,0.000008951248,0.00000276614,0.9945852,0.0002194127,0.004643475,0.0003640488,0.000002462164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04502015,0.0006845666,0.9461776,0.001059279,0.0001346542,0.00004894759,0.001223426,0.003448672,0.002202597],"genre_scores_gemma":[0.6858534,0.0004469426,0.3024631,0.001045758,0.0001743482,0.0002431969,0.004368468,0.0004443623,0.004960406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01412295,"threshold_uncertainty_score":0.02808154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0273075617153716,"score_gpt":0.2394422369428762,"score_spread":0.2121346752275046,"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."}}