{"id":"W4289516631","doi":"10.1108/ijccsm-11-2020-0124","title":"Climate change and its impact on rice acreage in high-latitude regions of China: an estimation by machine learning","year":2022,"lang":"en","type":"article","venue":"International Journal of Climate Change Strategies and Management","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"China Scholarship Council","keywords":"Climate change; Quantile; Latitude; Agriculture; Econometrics; Yield (engineering); Environmental science; China; Precipitation; Economics; Quantile regression; Agricultural economics; Mathematics; Agricultural engineering; Geography; Meteorology; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004746381,0.000186136,0.0002579241,0.0001120169,0.0001615302,0.0001634684,0.0002708104,0.00004039273,0.000141164],"category_scores_gemma":[0.00001202413,0.00008693457,0.00006417764,0.0002012376,0.00002544911,0.0008674242,0.0002660528,0.0002363428,6.870504e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009062396,"about_ca_system_score_gemma":0.000002444728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003960307,"about_ca_topic_score_gemma":0.0002824939,"domain_scores_codex":[0.9986009,0.0001130013,0.0003787599,0.0001949244,0.0004579975,0.000254388],"domain_scores_gemma":[0.9991972,0.00006311934,0.0005222263,0.00003640605,0.00008245307,0.00009858574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.007543274,0.006705239,0.04063198,0.001489487,0.001266571,0.001762009,0.02701785,0.0156813,0.0829851,0.1443745,0.001012773,0.66953],"study_design_scores_gemma":[0.001956213,0.004492111,0.9647842,0.0005339216,0.00009540646,0.0002213048,0.0169055,0.00763798,0.0001613966,0.001650283,0.001066978,0.0004947244],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947747,0.001027199,0.000003275687,0.002891817,0.0001762077,0.0003146582,0.0004766236,0.00001616648,0.0003193707],"genre_scores_gemma":[0.972534,0.02680646,0.00005039945,0.000166918,0.0001586273,0.00003579222,0.000237637,0.000003030128,0.000007142437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9241522,"threshold_uncertainty_score":0.3545089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04581997238870165,"score_gpt":0.3083484438174176,"score_spread":0.2625284714287159,"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."}}