{"id":"W4317877301","doi":"10.21203/rs.3.rs-3363027/v1","title":"Plant miRNAs in the rhizosphere target microbial genes","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Silicon Effects in Agriculture","field":"Agricultural and Biological Sciences","cited_by":3,"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; microRNA; Gene; Biology; Computational biology; Genetics; Bacteria","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":[0.000107348,0.0004196037,0.0002967488,0.0003137929,0.0002078276,0.0004181468,0.0001450831,0.0002935189,0.0008531851],"category_scores_gemma":[0.0001377131,0.0001734211,0.0002779271,0.0002067816,0.0001720814,0.0001979897,0.0003093117,0.0003159241,0.000550119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002420098,"about_ca_system_score_gemma":0.0002126007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000424618,"about_ca_topic_score_gemma":0.0004182128,"domain_scores_codex":[0.9998348,0.00001562485,0.00001048667,0.00007239252,0.00003615633,0.0000305708],"domain_scores_gemma":[0.9998075,0.00003087692,0.00006440937,0.00001669677,0.00003679896,0.00004377618],"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":[0.00004601638,0.000003017173,0.0004934985,0.00002054009,0.000002704061,0.00003069731,0.00001134566,0.00002800727,0.9987049,0.00004664885,0.00001233636,0.0006001379],"study_design_scores_gemma":[0.00001318085,0.0002167236,0.0631052,0.00002103443,0.00005162533,0.0006307164,0.00014523,0.002086286,0.927145,0.0003255952,0.006244304,0.00001516052],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893635,0.002438357,0.005666114,0.00008286307,0.00002662546,0.00001881549,0.0008911983,0.0001206365,0.001391913],"genre_scores_gemma":[0.9922194,0.0004378321,0.00399876,0.00004621365,0.000008549448,0.00002305113,0.001153803,0.00003124217,0.00208111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008531851,"threshold_uncertainty_score":0.002854168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08950674080598478,"score_gpt":0.3427300003891282,"score_spread":0.2532232595831434,"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."}}