{"id":"W1985479900","doi":"10.1080/01904167.2011.587584","title":"INFLUENCE OF TWENTY-THREE ANNUAL APPLICATIONS OF NITROGEN AND SULFUR FERTILIZERS, AND ONE-TIME LIMING ON DRY MATTER YIELD OF GRASS AND SOME SOIL PROPERTIES ON A DARK GRAY CHERNOZEM SOIL","year":2011,"lang":"en","type":"article","venue":"Journal of Plant Nutrition","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Agriculture Food and Rural Development; Agriculture and Agri-Food Canada","funders":"","keywords":"Chernozem; Agronomy; Lime; Loam; Soil water; Dry matter; Organic matter; Fertilizer; Perennial plant; Nitrogen; Chemistry; Calcareous; Soil horizon; Environmental science; Soil science; Botany; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001951713,0.00009978557,0.0002546306,0.00005242712,0.00004781863,0.00001035091,0.00008587859,0.00008029983,0.000004992017],"category_scores_gemma":[0.00002230935,0.00004696984,0.00005120173,0.00008338942,0.0001216067,0.0001477903,0.00002771711,0.0001120386,5.368057e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009452069,"about_ca_system_score_gemma":0.000008552704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002352463,"about_ca_topic_score_gemma":0.0001383331,"domain_scores_codex":[0.9992077,0.00003610161,0.0003455803,0.0001122588,0.0001947883,0.0001035579],"domain_scores_gemma":[0.9993254,0.00009035382,0.0003532339,0.00003769878,0.0001268603,0.0000664465],"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":[0.00139829,0.0006670838,0.1645467,0.0002516363,0.00007370597,0.000004084379,0.000399937,0.000008027888,0.8297552,0.0001287969,0.00005909595,0.002707474],"study_design_scores_gemma":[0.0008642073,0.002389776,0.7575572,0.00112873,0.0001223417,0.00007898591,0.001006445,0.0001705092,0.230615,0.005831735,0.00002640528,0.000208692],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986995,0.0007454384,8.786691e-7,0.0001923904,0.000009913838,0.0001480465,0.0001723098,0.000004089101,0.00002744743],"genre_scores_gemma":[0.9992929,0.0005155369,0.00007320829,0.00005716687,0.00004213872,0.000006492101,0.000007698574,0.000001399023,0.000003515522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5991402,"threshold_uncertainty_score":0.1915374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02111763691022129,"score_gpt":0.1884080434789179,"score_spread":0.1672904065686966,"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."}}