{"id":"W4389579251","doi":"10.1093/database/baad088","title":"Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences","year":2023,"lang":"en","type":"review","venue":"Database","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Saskatchewan Research Council (Canada)","funders":"Réseau de cancérologie Rossy; U.S. Department of Agriculture; National Institute of Food and Agriculture; National Science Foundation","keywords":"Data science; Raw data; Context (archaeology); Standardization; Computer science; Metadata; Annotation; Data collection; Biology; World Wide Web; Bioinformatics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.05321373,0.001288994,0.002005276,0.009812907,0.002026026,0.01168648,0.004831157,0.002566015,0.003356633],"category_scores_gemma":[0.1056454,0.0009612353,0.002496605,0.0186835,0.002592642,0.02045002,0.01194118,0.0060349,0.003344132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003213364,"about_ca_system_score_gemma":0.01201407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006570322,"about_ca_topic_score_gemma":0.00626992,"domain_scores_codex":[0.9759942,0.008639233,0.00447876,0.004229757,0.005683137,0.0009748001],"domain_scores_gemma":[0.8444838,0.04178508,0.008656894,0.06683469,0.03236786,0.005871669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005986028,0.0002458039,0.04761225,0.009055054,0.0006364083,0.0007868964,0.005266772,0.005391853,0.01888819,0.155398,0.2326178,0.5235025],"study_design_scores_gemma":[0.00005826111,0.0000706922,0.02310181,0.003797244,0.0002649369,0.0003499019,0.002671994,0.004436042,0.007445463,0.1235514,0.8340174,0.0002347424],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.03971143,0.03330616,0.5928416,0.06804824,0.005048602,0.002004835,0.2082171,0.02303337,0.02778863],"genre_scores_gemma":[0.06490731,0.01971478,0.5948552,0.01156601,0.001584593,0.002851008,0.2954786,0.006094149,0.002948279],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.05321373,"threshold_uncertainty_score":0.2814245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.298928971651219,"score_gpt":0.3947060009767093,"score_spread":0.09577702932549031,"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."}}