{"id":"W2902424612","doi":"10.1016/b978-0-12-811050-8.00004-2","title":"Potential of Increased Temporal Crop Diversity to Improve Resource Use Efficiencies","year":2018,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Diversity (politics); Resource (disambiguation); Crop; Resource use; Crop diversity; Agroforestry; Geography; Computer science; Environmental science; Environmental resource management; Forestry; Sociology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005444714,0.0002385053,0.0001590491,0.000252797,0.0001668859,0.0007470428,0.0004092359,0.0003742641,0.007568676],"category_scores_gemma":[0.0006403765,0.00008182025,0.0002979394,0.0004785479,0.000250223,0.001044283,0.0006077845,0.0004101277,0.0007521578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005016271,"about_ca_system_score_gemma":0.0004730607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001140662,"about_ca_topic_score_gemma":0.002697941,"domain_scores_codex":[0.9999093,0.00002042184,0.000005308661,0.00002462799,0.0000258027,0.00001452934],"domain_scores_gemma":[0.9997466,0.000112741,0.00003934557,0.00003938649,0.00003457585,0.00002731049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002828278,0.0002627925,0.008510171,0.0005949125,0.00014516,0.0001773964,0.0001979609,0.03239556,0.1486027,0.03988805,0.004602865,0.7643394],"study_design_scores_gemma":[0.000213013,0.002149338,0.1194851,0.0007729258,0.0005536985,0.001791987,0.00110362,0.09438021,0.1406321,0.2365397,0.4022229,0.0001553418],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6480495,0.0171723,0.1166888,0.004466552,0.0005161145,0.00005293837,0.0008894472,0.0007646239,0.2113997],"genre_scores_gemma":[0.9184527,0.005913877,0.04550355,0.0004661085,0.0001554661,0.00005100845,0.0003054684,0.0001407633,0.02901097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007568676,"threshold_uncertainty_score":0.02531976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531262997229429,"score_gpt":0.1980028708184934,"score_spread":0.1826902408461991,"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."}}