{"id":"W2344165886","doi":"10.1038/nclimate3007","title":"Gaps in agricultural climate adaptation research","year":2016,"lang":"en","type":"article","venue":"Nature Climate Change","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Agriculture; Adaptation (eye); Climate change adaptation; Climate change; Value (mathematics); Environmental resource management; Climate science; Political science; Regional science; Environmental planning; Natural resource economics; Geography; Environmental science; Economics; Ecology; Psychology; Computer science","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":[0.00118214,0.0003430536,0.0003368796,0.00009778613,0.0003040816,0.0001035648,0.0005241322,0.0007585434,0.001085632],"category_scores_gemma":[0.000260015,0.00009525617,0.0001345269,0.001529829,0.0001113152,0.0007028473,0.0002939735,0.0008132873,0.0005207564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002547842,"about_ca_system_score_gemma":0.000004885428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001574721,"about_ca_topic_score_gemma":0.008920874,"domain_scores_codex":[0.996428,0.0003016903,0.0003909465,0.0007000178,0.0006606564,0.001518701],"domain_scores_gemma":[0.9985862,0.0005160915,0.0001503728,0.000138324,0.0003524467,0.0002565382],"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":[0.0003788577,0.0005778466,0.02783798,0.0001179942,0.00001828125,0.0001275808,0.004644103,1.969617e-7,0.8087493,0.007446312,0.005028044,0.1450735],"study_design_scores_gemma":[0.0006125188,0.0003719971,0.9730118,0.0005937196,0.00001022215,0.0000402229,0.006156269,0.000003987813,0.00554512,0.0007326583,0.01236858,0.0005529241],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798039,0.001515108,6.213124e-8,0.009926287,0.0004281388,0.0008226174,0.0005621954,0.0002097535,0.006732],"genre_scores_gemma":[0.9924555,0.005119516,0.00004332271,0.0005120366,0.001305359,0.0002201109,0.0002383239,0.000004770965,0.0001010992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9451738,"threshold_uncertainty_score":0.9998275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1358682279761447,"score_gpt":0.3442601080889588,"score_spread":0.2083918801128142,"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."}}