{"id":"W2611404668","doi":"10.1016/j.agsy.2017.04.008","title":"Accurate crop yield predictions from modelling tree-crop interactions in gliricidia-maize agroforestry","year":2017,"lang":"en","type":"article","venue":"Agricultural Systems","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Centre for International Agricultural Research; Consortium of International Agricultural Research Centers; Commonwealth Scientific and Industrial Research Organisation; Natural Environment Research Council; Sight Research UK; Canadian International Development Agency","keywords":"Gliricidia; Gliricidia sepium; Monoculture; Agronomy; Crop; Agroforestry; Crop yield; Mathematics; Environmental science; 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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0001846347,0.0004579026,0.000521914,0.00002862141,0.001172669,0.001227132,0.0009199453,0.0002673412,0.0002760539],"category_scores_gemma":[0.0001910232,0.0001550607,0.0002387798,0.0003524502,0.00008899924,0.001295154,0.000233449,0.0005492341,0.0002368696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002323014,"about_ca_system_score_gemma":0.000009935573,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03824037,"about_ca_topic_score_gemma":0.04213264,"domain_scores_codex":[0.9973838,0.0001129373,0.0006678464,0.0006845111,0.0004176175,0.0007332935],"domain_scores_gemma":[0.9982237,0.0003741902,0.0005893596,0.0002840234,0.0002109923,0.0003177499],"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":[0.0002020961,0.001038382,0.09296461,0.00009124617,0.0003531282,0.000216013,0.001920805,0.02412121,0.8085145,0.0004647617,0.06237878,0.007734523],"study_design_scores_gemma":[0.0003566957,0.0001033777,0.9785338,0.0005343346,0.00005593429,0.00007271011,0.004272251,0.005894569,0.0008145126,0.00006657474,0.008624976,0.000670278],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879919,0.0004186701,0.00002277625,0.002933305,0.001835139,0.0007061259,0.0006129239,0.0002287921,0.005250327],"genre_scores_gemma":[0.9947804,0.0002017432,0.00002805541,0.00004379421,0.002032629,0.0001417275,0.0005607794,0.000003799206,0.002207106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8855692,"threshold_uncertainty_score":0.9998097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1050981341500358,"score_gpt":0.2811074973553587,"score_spread":0.176009363205323,"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."}}