{"id":"W2040310909","doi":"10.1371/journal.pone.0121800","title":"Genome-Wide Identification, Characterization and Evolutionary Analysis of Long Intergenic Noncoding RNAs in Cucumber","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Plant and Fungal Interactions Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics and Developmental Biology, Chinese Academy of Sciences; Program for New Century Excellent Talents in University; Shaanxi Normal University; Chinese Academy of Sciences; Ministry of Education of the People's Republic of China; Institute of Genetics; National Natural Science Foundation of China","keywords":"Biology; Intergenic region; Cucumis; Computational biology; Identification (biology); Transcriptome; Gene; Genetics; Genome; Gene expression; Botany","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001542814,0.00004922879,0.0001124326,0.0002218528,0.00001859748,0.00001257713,0.00006856589,0.00004930792,0.00002561821],"category_scores_gemma":[0.0001407084,0.00005280542,0.00002483618,0.0002946192,0.00002626634,0.00001181846,0.00005656249,0.00005177608,0.000007182335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002348417,"about_ca_system_score_gemma":0.0000280073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002965539,"about_ca_topic_score_gemma":0.0001622145,"domain_scores_codex":[0.9994175,0.00004020972,0.0001868716,0.0001499316,0.0001198887,0.0000856017],"domain_scores_gemma":[0.9995908,0.00001233146,0.00006187507,0.000127104,0.0001631308,0.00004476687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003816185,0.0001350278,0.3191416,0.00000965039,0.0002787599,8.39549e-7,0.00004956606,0.00001870647,0.680245,0.000001992013,0.00001115758,0.00006958158],"study_design_scores_gemma":[0.0001572262,0.00004513134,0.7499487,0.00001904418,0.0001492837,0.000001484084,0.00003974516,0.003513266,0.2458835,0.000005311981,0.0001653274,0.00007201609],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979624,0.0002239188,0.001443572,0.00005788972,0.00001643647,0.00007879479,0.00002990919,0.000003072962,0.0001840241],"genre_scores_gemma":[0.9972374,0.0002831095,0.0000970058,0.00001266798,0.00003510654,0.00001360104,0.001124551,0.000005052663,0.001191532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4343615,"threshold_uncertainty_score":0.2153342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04806886287578473,"score_gpt":0.2792651265388349,"score_spread":0.2311962636630502,"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."}}