{"id":"W3007637688","doi":"10.1186/s13104-020-4922-8","title":"Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets","year":2020,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Agricultural Research Service; Johns Hopkins University; Michigan State University; College of Engineering, Michigan State University; U.S. Department of Agriculture; National Institute of Food and Agriculture; National Science Foundation","keywords":"Metadata; Context (archaeology); Outlier; Phenomics; Population; Data science; Genome; Computer science; Biology; Geography; Genomics; Artificial intelligence; Medicine; Genetics; World Wide Web","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":[0.001783452,0.001395978,0.0009619357,0.003044526,0.001158855,0.001113037,0.002492098,0.001165815,0.0142422],"category_scores_gemma":[0.004090874,0.0006154496,0.00112189,0.006460728,0.0004167568,0.001295278,0.00201158,0.001125885,0.0132103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001928506,"about_ca_system_score_gemma":0.00259425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04151588,"about_ca_topic_score_gemma":0.06906617,"domain_scores_codex":[0.9990913,0.00009041361,0.0000710289,0.0002880731,0.0002904426,0.0001688149],"domain_scores_gemma":[0.9983557,0.0001770997,0.0002498083,0.0004186653,0.0005268734,0.000271872],"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":[0.0006830961,0.0001530843,0.01233517,0.001024037,0.0002247315,0.0001594427,0.0004687486,0.001576423,0.009422533,0.003089617,0.9554297,0.01543334],"study_design_scores_gemma":[0.0008531343,0.0001137924,0.1026156,0.0002341808,0.0001236879,0.0001480419,0.0003479624,0.001460135,0.004400226,0.002994886,0.8865747,0.000133585],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003303202,0.00005112473,0.0006086926,0.00009423863,0.00002358895,0.00004455659,0.9938551,0.0009972757,0.001022295],"genre_scores_gemma":[0.001598401,0.00002423321,0.00145986,0.00002942789,0.00000497781,0.00009812655,0.9964048,0.000111967,0.0002682929],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04151588,"threshold_uncertainty_score":0.0825485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0932418440745603,"score_gpt":0.3511229544297724,"score_spread":0.2578811103552121,"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."}}