{"id":"W2279711895","doi":"10.1088/1748-9326/11/3/034003","title":"Changes in yield variability of major crops for 1981–2010 explained by climate change","year":2016,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":260,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Yield (engineering); Climate change; Environmental science; Agronomy; Precipitation; Crop; Crop yield; Abiotic component; Range (aeronautics); Climatology; Geography; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001373683,0.0001732209,0.0002223093,0.00003559482,0.0001333564,0.00002640733,0.0003599864,0.0001285514,0.001228538],"category_scores_gemma":[0.0002797198,0.00005916904,0.00007587741,0.0002108947,0.0002800138,0.0001898793,0.000225589,0.0001647293,0.00004206723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000213819,"about_ca_system_score_gemma":0.000001581332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000256849,"about_ca_topic_score_gemma":0.001503366,"domain_scores_codex":[0.9978068,0.0002071933,0.0002247745,0.0004428951,0.0004958851,0.0008224678],"domain_scores_gemma":[0.9982836,0.00133889,0.00007957173,0.0001200793,0.00001181073,0.0001661094],"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":[0.0001162081,0.0001899243,0.04683811,0.00001928546,0.000005079283,0.000002996831,0.0001392904,2.254904e-8,0.932534,0.000009764469,0.004227282,0.01591797],"study_design_scores_gemma":[0.0007929595,0.0007415155,0.7970923,0.0001583922,0.000006413227,0.000003212469,0.0008418137,0.000004316713,0.1915517,0.0001312052,0.008335733,0.0003403838],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9454169,0.00007365691,0.000001864036,0.05171053,0.00006862007,0.0009752478,0.001692966,0.00001858247,0.0000415813],"genre_scores_gemma":[0.9978873,0.0004879803,0.00003696998,0.0007162087,0.0002177683,0.0003882896,0.0001248001,0.000003127269,0.0001375248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7502542,"threshold_uncertainty_score":0.9996845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08333684700898437,"score_gpt":0.289914211904958,"score_spread":0.2065773648959736,"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."}}