{"id":"W2104322614","doi":"10.1111/gcb.13061","title":"Climate change and maize yield in southern Africa: what can farm management do?","year":2015,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department for International Development; Department for International Development, UK Government; International Development Research Centre","keywords":"Climate change; Yield (engineering); Agroforestry; Crop management; Agronomy; Geography; Environmental science; Agricultural economics; Ecology; Economics; Crop; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003783352,0.000276386,0.0001646046,0.0001817796,0.0001862055,0.0004807541,0.0001419528,0.0002957752,0.0007166553],"category_scores_gemma":[0.001293755,0.00009313368,0.0002338777,0.0004562609,0.0002230601,0.0005160042,0.0002127753,0.0001684843,0.00005979257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007367936,"about_ca_system_score_gemma":0.0003079814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02989215,"about_ca_topic_score_gemma":0.04631453,"domain_scores_codex":[0.9999219,0.00003144069,0.000003098421,0.00001088664,0.000009599784,0.00002302168],"domain_scores_gemma":[0.9997265,0.00008634773,0.0001114604,0.00001201354,0.00003026651,0.00003341122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002448009,0.00006656317,0.9438323,0.0001567869,0.0001703876,0.0003267178,0.0008994706,0.02525135,0.007160732,0.0007950146,0.0004700444,0.02062589],"study_design_scores_gemma":[0.00001069384,0.00009006475,0.9831181,0.00003206207,0.00006090542,0.00004209251,0.0008069635,0.01369401,0.0005382338,0.0005135164,0.001083506,0.00001000123],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998507,0.0004470678,0.0001584231,0.0003269722,0.000002669748,0.000002710237,0.0001101097,0.000004028413,0.0004411507],"genre_scores_gemma":[0.9994372,0.0003231734,0.00009918977,0.00002108952,0.000003604208,0.000001988063,0.00004506173,0.000001185285,0.00006752231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02989215,"threshold_uncertainty_score":0.05943632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1699866841029117,"score_gpt":0.2766746812242871,"score_spread":0.1066879971213754,"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."}}