{"id":"W4318472573","doi":"10.37867/te1401141","title":"GENOME-WIDE IDENTIFICATION OF NICOTIANA TABACUM MIRNAS AND THEIR ROLE IN HUMAN HEALTH – A COMPUTATIONAL GENOMICS ASSESSMENT","year":2022,"lang":"en","type":"article","venue":"Towards Excellence","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Nicotiana tabacum; Genome; Biology; microRNA; Identification (biology); Genomics; Computational biology; Biotechnology; Genetics; Botany; Gene","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":[0.0004564771,0.0005678058,0.0007625438,0.0008143779,0.0005028018,0.0006655542,0.0005366985,0.0006531678,0.002390992],"category_scores_gemma":[0.0008942538,0.0002196772,0.002014997,0.0007061962,0.0001985875,0.0002706915,0.0004242959,0.0006076675,0.0005274179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003990114,"about_ca_system_score_gemma":0.000791888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01057972,"about_ca_topic_score_gemma":0.01796433,"domain_scores_codex":[0.9998168,0.00004334942,0.000007945727,0.00009046047,0.00001657726,0.00002484427],"domain_scores_gemma":[0.9997552,0.0001782035,0.00001421973,0.00001620261,0.0000190544,0.00001711676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004383826,0.001401676,0.3364658,0.005103774,0.006193999,0.003378311,0.0006522606,0.3839554,0.06533144,0.00767595,0.04790306,0.1375544],"study_design_scores_gemma":[0.0003273462,0.000720995,0.1095928,0.0002073242,0.002195527,0.001152598,0.000744398,0.8394914,0.007972954,0.01280677,0.02469335,0.00009454924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8921875,0.00656446,0.02762209,0.002405324,0.0001677225,0.00009513566,0.0639537,0.002530871,0.004473276],"genre_scores_gemma":[0.8648008,0.001777225,0.02679119,0.0006574189,0.00005179733,0.0001223922,0.1034226,0.0001784931,0.00219817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01057972,"threshold_uncertainty_score":0.02103627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008579901545675967,"score_gpt":0.2967978915193404,"score_spread":0.2882179899736645,"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."}}