{"id":"W3014624313","doi":"10.1038/s41438-020-0261-0","title":"Coriander Genomics Database: a genomic, transcriptomic, and metabolic database for coriander","year":2020,"lang":"en","type":"article","venue":"Horticulture Research","topic":"Plant Gene Expression Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Fredericton; Agriculture and Agri-Food Canada","funders":"National Natural Science Foundation of China","keywords":"Biology; Coriandrum; Apiaceae; Genome; Genomics; Proteogenomics; Gene; Functional genomics; Database; Comparative genomics; Sativum; Transcriptome; Genetics; Computational biology; Botany; Gene expression","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":[0.001002104,0.002132492,0.001642777,0.005775428,0.0009556877,0.001677687,0.002645836,0.001272595,0.02075808],"category_scores_gemma":[0.001796759,0.0006360639,0.0009372035,0.006973467,0.000349084,0.00211397,0.002372545,0.001538244,0.01746437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001174252,"about_ca_system_score_gemma":0.003167717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007920454,"about_ca_topic_score_gemma":0.009824162,"domain_scores_codex":[0.9994747,0.00004715639,0.00009179481,0.0002000449,0.0001169028,0.00006944623],"domain_scores_gemma":[0.9990853,0.0001050986,0.0001851888,0.0002378444,0.0001815485,0.0002050105],"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":[0.002541437,0.000356386,0.01031537,0.01081513,0.0004383191,0.001615341,0.001280555,0.001497758,0.3794802,0.006894881,0.4394439,0.1453206],"study_design_scores_gemma":[0.0004728735,0.0002007709,0.02685417,0.0005545857,0.0003290988,0.001033588,0.0002782066,0.003174131,0.03239432,0.002663977,0.9318826,0.0001616147],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.0124488,0.002521785,0.01373817,0.0002229774,0.0000795429,0.0003543656,0.9385934,0.02398952,0.008051385],"genre_scores_gemma":[0.009210522,0.0007255263,0.0173222,0.000121996,0.00001396924,0.0002787963,0.9698257,0.001256365,0.001245007],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02075808,"threshold_uncertainty_score":0.06944275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07519983342525158,"score_gpt":0.3472170513727788,"score_spread":0.2720172179475272,"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."}}