{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007679857,0.0001663566,0.0002496687,0.00004476187,0.0001731495,0.00009254667,0.0006258388,0.00006475145,4.681732e-7],"category_scores_gemma":[0.0003768627,0.00009853124,0.0000146483,0.0002026895,0.0001078319,0.000005377403,0.0009297686,0.00006143079,7.773967e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004545956,"about_ca_system_score_gemma":0.0001262021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003654126,"about_ca_topic_score_gemma":0.000817704,"domain_scores_codex":[0.998868,0.00008548394,0.0002298977,0.0005965108,0.0000922543,0.0001278195],"domain_scores_gemma":[0.9989625,0.00007776346,0.0001103231,0.0007807007,0.00004815573,0.00002057312],"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":[0.00001294961,0.00002607585,0.00001868315,0.001510176,0.00007286683,6.70105e-7,0.00006341832,0.000001794952,0.0002350598,0.0005953567,0.009048839,0.9884141],"study_design_scores_gemma":[0.0001414052,0.00009591212,0.0001169478,0.0003585669,0.0001870144,0.000005267623,0.0001238941,0.0001166797,0.000005708103,0.00004807277,0.9986356,0.0001648777],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002046834,0.9794171,0.002499537,0.00005593085,0.0001580213,0.000698688,0.0169405,0.000002556195,0.00002293904],"genre_scores_gemma":[0.0002913861,0.9294894,0.000776704,0.00003207946,0.0002813865,0.00003561918,0.06906658,0.00001249162,0.00001438313],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9895868,"threshold_uncertainty_score":0.4017988,"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."}}