{"id":"W3131420028","doi":"10.1101/2021.02.19.431941","title":"Relative and quantitative rhizosphere microbiome profiling result in distinct abundance patterns","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Agriculture and Agri-Food Canada; National Research Council Canada","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Compute Canada; Armand-Frappier Foundation","keywords":"Rhizosphere; Relative species abundance; Abundance (ecology); Microbiome; Biology; Amplicon sequencing; Metagenomics; Microbial population biology; Amplicon; Phylum; Ecology; 16S ribosomal RNA; Bacteria; Genetics; Gene; Polymerase chain reaction","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":[0.0008025515,0.0004205333,0.0003618458,0.0005554836,0.0001532453,0.0006710574,0.0001732231,0.0005195339,0.001047162],"category_scores_gemma":[0.0007966449,0.0002245966,0.0002626046,0.0004834496,0.0004330288,0.0004008846,0.0004085405,0.0005594866,0.0004713376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001741538,"about_ca_system_score_gemma":0.0001311008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003646575,"about_ca_topic_score_gemma":0.0004721147,"domain_scores_codex":[0.9991309,0.0001672935,0.00004629149,0.0002926723,0.0002603808,0.000102506],"domain_scores_gemma":[0.9994969,0.0001842496,0.0001198919,0.00005901706,0.00009677242,0.000043212],"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":[0.0000855852,0.00001646199,0.001858187,0.00003876358,0.00001130401,0.000009716965,0.00002311972,0.00006395662,0.9959421,0.0000469179,0.00003464441,0.001869261],"study_design_scores_gemma":[0.00001586486,0.0003090245,0.1491206,0.00002329381,0.00005009767,0.0003283031,0.0003053245,0.005362391,0.8413596,0.000647826,0.002442701,0.00003497675],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9514357,0.00117076,0.04162537,0.0001790348,0.00006598628,0.00005214839,0.003526957,0.0003386621,0.001605263],"genre_scores_gemma":[0.9749498,0.000313752,0.02063602,0.0001596159,0.00001751038,0.00008274921,0.00226953,0.00008991067,0.001480948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001047162,"threshold_uncertainty_score":0.004244328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01445266438096535,"score_gpt":0.2285016838622407,"score_spread":0.2140490194812754,"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."}}