{"id":"W4288058097","doi":"10.1038/s43705-022-00151-2","title":"The response of wheat and its microbiome to contemporary and historical water stress in a field experiment","year":2022,"lang":"en","type":"article","venue":"ISME Communications","topic":"Plant-Microbe Interactions and Immunity","field":"Agricultural and Biological Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Institut National de la Recherche Scientifique; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Compute Canada; Fonds de recherche du Québec – Nature et technologies; Armand-Frappier Foundation","keywords":"Biology; Water stress; Microbiome; Amplicon sequencing; Ecology; Agronomy; 16S ribosomal RNA; Bacteria; Bioinformatics","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":[0.0003625524,0.0003328536,0.0004619916,0.0001741669,0.0006111087,0.0004134544,0.0002509922,0.0004773858,0.0007010194],"category_scores_gemma":[0.0003219776,0.0002068478,0.0003332912,0.0001576945,0.0003917209,0.0003770747,0.00042536,0.0007897361,0.0001054171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005966548,"about_ca_system_score_gemma":0.0003979795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003211081,"about_ca_topic_score_gemma":0.007136616,"domain_scores_codex":[0.9997388,0.00004675383,0.00001481411,0.0001244665,0.00003507492,0.00004025629],"domain_scores_gemma":[0.9995414,0.00007994248,0.00008111996,0.0000545423,0.00007002481,0.0001728825],"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":[0.001164528,0.0006097188,0.01506408,0.00003740452,0.00003540581,0.00004765663,0.0002319003,0.0001132107,0.9809499,0.00003971466,0.00005936861,0.001647206],"study_design_scores_gemma":[0.0001745209,0.02115053,0.8150305,0.00001378283,0.0001817556,0.0001825708,0.0009324126,0.001666982,0.1573824,0.0002385822,0.002979707,0.00006611216],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990793,0.00005320511,0.0004817978,0.00002160335,0.00001150957,0.00004805132,0.0001529868,0.000006572783,0.0001448285],"genre_scores_gemma":[0.9962813,0.00008882827,0.001816783,0.0001779501,0.00001493681,0.0002311174,0.0005995002,0.000008325101,0.0007812607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003211081,"threshold_uncertainty_score":0.00638479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06081424847274703,"score_gpt":0.26901767814266,"score_spread":0.208203429669913,"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."}}