{"id":"W4200226918","doi":"10.3389/fpls.2021.761402","title":"GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley","year":2021,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Mycotoxins in Agriculture and Food","field":"Agricultural and Biological Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Western University; University of Manitoba","funders":"Agriculture and Agri-Food Canada; Centers for Disease Control and Prevention; Western Grains Research Foundation","keywords":"Fusarium; Biology; Trichothecene; Molecular breeding; Germplasm; Biotechnology; Single-nucleotide polymorphism; Hordeum vulgare; Mycotoxin; Genetics; Plant breeding; Agronomy; Quantitative trait locus; Genotype; Poaceae; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004397683,0.000887961,0.0004051379,0.0004897455,0.0002311443,0.0004257933,0.0009807784,0.0006881616,0.002881458],"category_scores_gemma":[0.0009391284,0.0002438505,0.0006657502,0.0004594798,0.0002075866,0.0004145466,0.0004997051,0.001113134,0.0007712293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007370071,"about_ca_system_score_gemma":0.0007452861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01436146,"about_ca_topic_score_gemma":0.01264006,"domain_scores_codex":[0.9998873,0.00001873461,0.000006218523,0.0000419254,0.00001897395,0.00002679773],"domain_scores_gemma":[0.999803,0.0001000727,0.0000158662,0.00001931451,0.00004912693,0.00001268299],"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":[0.0006214528,0.0003130989,0.00909969,0.0001358259,0.0002027871,0.0003206715,0.00008482933,0.480091,0.02450249,0.001771822,0.01049501,0.4723613],"study_design_scores_gemma":[0.00001138229,0.00002944407,0.0005117735,0.000003447622,0.000008693331,0.00001450451,0.000008535046,0.9966979,0.00163051,0.0006435219,0.0004365316,0.000003789514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2619493,0.0008735268,0.7170959,0.0004766792,0.0001289931,0.0001293776,0.00268239,0.01393592,0.002727919],"genre_scores_gemma":[0.811335,0.0003646398,0.1750784,0.0003234411,0.00004277648,0.0002060873,0.005848169,0.0004413573,0.006360213],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01436146,"threshold_uncertainty_score":0.02855575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009948712302992716,"score_gpt":0.2211826427837397,"score_spread":0.211233930480747,"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."}}