{"id":"W4288096403","doi":"10.1101/2022.07.26.501597","title":"Rhizospheric miRNAs affect the plant microbiota","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Plant Molecular Biology Research","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Centre National de la Recherche Scientifique; National Research Council Canada; Compute Canada","keywords":"Rhizosphere; Biology; Brachypodium distachyon; Arabidopsis; Arabidopsis thaliana; microRNA; Bacteria; Botany; Gene; Mutant; Genetics; Genome","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001486222,0.0003829408,0.0002977333,0.0001337895,0.0001480283,0.0004424447,0.0001353352,0.0003906264,0.000928644],"category_scores_gemma":[0.0001191445,0.0001608219,0.0001822289,0.00007424327,0.00020151,0.0001849392,0.0004081328,0.0005215398,0.0003939327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002549581,"about_ca_system_score_gemma":0.0001306772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003438991,"about_ca_topic_score_gemma":0.0002760834,"domain_scores_codex":[0.9998357,0.00002768305,0.000007859434,0.00005725212,0.00003746856,0.00003402207],"domain_scores_gemma":[0.9998556,0.00002661298,0.00003878785,0.00001764779,0.00002151983,0.00003972965],"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":[0.00003560432,0.00000353222,0.0001007836,0.000008965058,0.000001314539,0.00001029426,0.000004975774,0.00001559605,0.9995303,0.00002874,0.00001190231,0.0002480258],"study_design_scores_gemma":[0.00001144,0.00009517212,0.009612758,0.000004610523,0.0000105476,0.00008172153,0.0000528021,0.0009890348,0.9866012,0.000147156,0.002387372,0.000006219789],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937629,0.001158193,0.002875747,0.0002026972,0.00005129741,0.00001600041,0.0003523107,0.0001216242,0.001459309],"genre_scores_gemma":[0.9950659,0.0002952452,0.001558317,0.00009118461,0.00001046496,0.00001118513,0.0002619847,0.00005092671,0.002654709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000928644,"threshold_uncertainty_score":0.003106594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040512736216097,"score_gpt":0.2144034252581538,"score_spread":0.1939982978959929,"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."}}