{"id":"W4320484645","doi":"10.1186/s12864-023-09159-x","title":"Clustering pattern and evolution characteristic of microRNAs in grass carp (Ctenopharyngodon idella)","year":2023,"lang":"en","type":"article","venue":"BMC Genomics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"Agriculture Research System of China; National Natural Science Foundation of China","keywords":"Biology; microRNA; Grass carp; Genetics; Computational biology; DNA microarray; Cluster analysis; Evolutionary biology; Regulation of gene expression; Gene; Gene expression; Fish <Actinopterygii>; Fishery","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":[0.0001108262,0.0001286181,0.0001657553,0.0009989224,0.0003056847,0.0002331245,0.0001558099,0.0002337368,0.0007799921],"category_scores_gemma":[0.0002923827,0.0001358887,0.0002158362,0.0004777632,0.0002516696,0.0001489917,0.0002067861,0.0002137391,0.0003431704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003033379,"about_ca_system_score_gemma":0.0001751473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001604852,"about_ca_topic_score_gemma":0.002422379,"domain_scores_codex":[0.9998645,0.000008865004,0.000007811745,0.00008185339,0.00002038283,0.00001657356],"domain_scores_gemma":[0.9997155,0.00004085924,0.0001270082,0.00001988299,0.00005082833,0.00004579528],"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":[0.0003679135,0.00002365981,0.06436758,0.00008993833,0.00003873337,0.0002138433,0.0003355878,0.0004375167,0.9234993,0.000345811,0.0001803013,0.01009981],"study_design_scores_gemma":[0.00001024989,0.0001836187,0.9491501,0.00001405378,0.00006090419,0.001327156,0.0002376015,0.002271214,0.04446971,0.0002749003,0.001962435,0.00003804626],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973245,0.0002732258,0.001148929,0.00001460706,0.000003198003,0.000007723069,0.000295936,0.00003288776,0.0008989892],"genre_scores_gemma":[0.9961689,0.00009065939,0.002073904,0.00001613209,0.000003624449,0.00002020657,0.0005143085,0.00002208703,0.001090196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001604852,"threshold_uncertainty_score":0.003191054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01412050903022757,"score_gpt":0.2362282589716241,"score_spread":0.2221077499413965,"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."}}