{"id":"W2146833082","doi":"10.1111/j.1466-8238.2010.00563.x","title":"Mind the gap: how do climate and agricultural management explain the ‘yield gap’ of croplands around the world?","year":2010,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":607,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"University of Wisconsin-Madison; National Aeronautics and Space Administration","keywords":"Yield gap; Yield (engineering); Agriculture; Crop yield; Climate change; Environmental science; Crop; Spatial ecology; Agroforestry; Ecology; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004264425,0.0002263345,0.0001995562,0.00001544578,0.0007844631,0.0002297754,0.0004271708,0.0001610779,0.0001429336],"category_scores_gemma":[0.00002641562,0.00004696371,0.0001327854,0.0007640719,0.0007584081,0.00008989233,0.0002757224,0.0002620519,0.000004451413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005942437,"about_ca_system_score_gemma":0.000001573536,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008908985,"about_ca_topic_score_gemma":0.01967699,"domain_scores_codex":[0.9987971,0.0001050059,0.0001804318,0.0003064916,0.0001646479,0.0004463244],"domain_scores_gemma":[0.9991654,0.0004035003,0.0001651562,0.0001192552,0.0000561978,0.00009054239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000117366,0.0001583188,0.9222763,0.00002652794,0.0002138777,0.0000144709,0.0002687454,3.324874e-7,0.01620546,0.006209488,0.01733186,0.03717732],"study_design_scores_gemma":[0.0001534547,0.0001558833,0.9647161,0.00001251427,0.00008184369,0.00009641155,0.004585299,0.0000014589,0.0001561887,0.0005448877,0.0293544,0.0001415966],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653323,0.0007023591,5.971211e-8,0.03027047,0.0003074122,0.0004596805,0.0003464612,0.00001854074,0.002562767],"genre_scores_gemma":[0.9977435,0.001001684,0.00001859669,0.0008524234,0.0002131318,0.00003303977,0.00004739486,6.518321e-7,0.00008955851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04243982,"threshold_uncertainty_score":0.9982113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01659121736270985,"score_gpt":0.2233743356437821,"score_spread":0.2067831182810722,"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."}}