{"id":"W4212829201","doi":"10.1002/crso.20179","title":"Evaluating Impacts of 4R Nutrient Stewardship","year":2022,"lang":"en","type":"article","venue":"Crops & Soils","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Plant Biotechnology Institute","funders":"","keywords":"Stewardship (theology); Livelihood; Agriculture; Food security; Business; Environmental stewardship; Life-cycle assessment; Carbon footprint; Environmental resource management; Tipping point (physics); Nutrient management; Quality (philosophy); Environmental planning; Environmental economics; Agricultural science; Agricultural engineering; Environmental science; Greenhouse gas; Production (economics); Engineering; Geography; Economics; Political science; Ecology","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":[0.03505667,0.0009513018,0.0007383736,0.002525879,0.001148346,0.004407695,0.001241407,0.001360572,0.01444366],"category_scores_gemma":[0.08704181,0.0002906354,0.001399922,0.002076707,0.001410255,0.004283431,0.005687509,0.001199151,0.002407643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006046419,"about_ca_system_score_gemma":0.005179799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009435902,"about_ca_topic_score_gemma":0.01471901,"domain_scores_codex":[0.959653,0.02245693,0.001455762,0.001885834,0.01280524,0.001743278],"domain_scores_gemma":[0.9242547,0.04215338,0.01055011,0.004564457,0.01308407,0.005393253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004441652,0.00440754,0.3370673,0.001029225,0.001318953,0.000351159,0.001745926,0.0428416,0.003154448,0.01816282,0.02058644,0.5648928],"study_design_scores_gemma":[0.0009896732,0.03363702,0.6098547,0.002955043,0.00224044,0.0005288449,0.01308481,0.1302444,0.02766872,0.05696126,0.1212106,0.0006244678],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7821442,0.001306029,0.01578586,0.005818355,0.0002785646,0.001958739,0.002852355,0.0007423898,0.1891134],"genre_scores_gemma":[0.9665661,0.0006890172,0.02233292,0.0007837174,0.00006639247,0.0005596581,0.001079334,0.0001218781,0.007800964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03505667,"threshold_uncertainty_score":0.1853996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02657739610462601,"score_gpt":0.2886615668936088,"score_spread":0.2620841707889828,"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."}}