{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003155765,0.000435241,0.0004078055,0.001007633,0.0005142684,0.0003624487,0.0001982021,0.0002962677,0.0007282926],"category_scores_gemma":[0.0003333592,0.0001700204,0.0003917507,0.001112302,0.0002536236,0.0002139589,0.0003066901,0.000320601,0.0003391516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005385158,"about_ca_system_score_gemma":0.0004977816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006170591,"about_ca_topic_score_gemma":0.01299093,"domain_scores_codex":[0.9997755,0.0000117348,0.00001641066,0.0001121948,0.00005429306,0.00002986564],"domain_scores_gemma":[0.999763,0.00004060394,0.0000760422,0.00001694268,0.00004185974,0.00006159477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002933104,0.00004029233,0.025202,0.0003713034,0.0000615111,0.0005163774,0.0002580457,0.0003280486,0.9582795,0.0001886448,0.0004731476,0.01398775],"study_design_scores_gemma":[0.00005361874,0.0003238892,0.9124841,0.00006583259,0.0002573037,0.001415605,0.0002425661,0.004264004,0.06424876,0.0002241131,0.01634256,0.0000776274],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977526,0.004778815,0.004656524,0.0001010699,0.00004152987,0.00009240389,0.01073278,0.0001714725,0.001899402],"genre_scores_gemma":[0.9606133,0.001982545,0.01353119,0.0002278437,0.0000259706,0.000123448,0.02000865,0.0001181981,0.003368876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006170591,"threshold_uncertainty_score":0.01226938,"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."}}