{"id":"W4289745142","doi":"10.3389/fsoil.2022.945888","title":"Spatial, temporal and technical variability in the diversity of prokaryotes and fungi in agricultural soils","year":2022,"lang":"en","type":"article","venue":"Frontiers in Soil Science","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut de Recherche et de Développement en Agroenvironnement","funders":"Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Spatial variability; Soil water; Environmental science; Sampling (signal processing); UniFrac; Grassland; Agriculture; Diversity index; Biology; Ecology; Agronomy; Soil science; Statistics; Mathematics; Species richness; Computer science","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":[0.003249352,0.0002335379,0.0003181927,0.0009655774,0.0003612622,0.0007317115,0.0002664743,0.0003684642,0.0002606127],"category_scores_gemma":[0.0043277,0.0001746441,0.0005159918,0.00139758,0.000505248,0.0003919222,0.0005539968,0.0002204325,0.0001210042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003646363,"about_ca_system_score_gemma":0.0003523879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002510466,"about_ca_topic_score_gemma":0.004925553,"domain_scores_codex":[0.9964645,0.0007146116,0.0004320204,0.001114843,0.001104578,0.0001693563],"domain_scores_gemma":[0.9940235,0.002466912,0.001474642,0.0005602849,0.001357486,0.0001171098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004430853,0.0001495176,0.6774226,0.00040759,0.0005149223,0.0002128278,0.00104818,0.00320945,0.2735647,0.0003064533,0.0002255503,0.04249505],"study_design_scores_gemma":[0.000003350262,0.0001668738,0.9780188,0.00001870216,0.00007057166,0.0002020273,0.0003063056,0.002038637,0.01783024,0.0002647456,0.001054808,0.00002492983],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867251,0.001056431,0.01079056,0.00003501189,0.00001444322,0.00002960649,0.0005710089,0.0000351058,0.0007427127],"genre_scores_gemma":[0.9940889,0.0002418666,0.004624807,0.00003309534,0.00001143921,0.0000499915,0.0007399367,0.00001232194,0.0001977068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003249352,"threshold_uncertainty_score":0.01718438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007621853000594016,"score_gpt":0.2022541711867101,"score_spread":0.1946323181861161,"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."}}