{"id":"W2064921624","doi":"10.1061/(asce)ir.1943-4774.0000895","title":"Corn Yield Simulation under Different Nitrogen Loading and Climate Change Scenarios","year":2015,"lang":"en","type":"article","venue":"Journal of Irrigation and Drainage Engineering","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"DSSAT; Environmental science; Climate change; Crop; Baseline (sea); Biomass (ecology); Yield (engineering); Growing season; Agronomy; Crop simulation model; Crop yield; Agriculture; Ecology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002538651,0.0005220952,0.0002358786,0.0002814461,0.0004171014,0.0004146107,0.000563013,0.0003671343,0.001092779],"category_scores_gemma":[0.000538378,0.0001529679,0.0003750748,0.0004270923,0.0002717077,0.0002170373,0.0001734188,0.0002789954,0.00007781491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00393899,"about_ca_system_score_gemma":0.001818663,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5765754,"about_ca_topic_score_gemma":0.5294562,"domain_scores_codex":[0.9999006,0.00001901125,0.000004400321,0.00002477111,0.00001707358,0.00003413393],"domain_scores_gemma":[0.999778,0.00007601006,0.00002265497,0.00001297554,0.00008189963,0.0000285369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002082183,0.00009351613,0.02884222,0.00002568451,0.00003846307,0.0001285859,0.00003012627,0.9652001,0.002785967,0.0002838769,0.0003991393,0.001964157],"study_design_scores_gemma":[0.00007063337,0.00007909339,0.03343626,0.00000347056,0.00002623986,0.00001485993,0.00008061861,0.9641391,0.001673846,0.00008026281,0.000381278,0.00001430555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976054,0.00002127507,0.0003555703,0.00002612609,0.000003971544,0.000009598034,0.0007453609,0.00003635994,0.001196417],"genre_scores_gemma":[0.9985267,0.00002238074,0.0003640249,0.000006435469,9.139308e-7,0.000008034377,0.0006928967,0.000006113129,0.0003724752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5765754,"threshold_uncertainty_score":0.8518364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06826401600548278,"score_gpt":0.2491493071105532,"score_spread":0.1808852911050704,"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."}}