{"id":"W4387843872","doi":"10.1016/j.jhydrol.2023.130376","title":"Assessing the contribution of China's grain production during 2005–2020 from the perspective of the crop-water-land nexus","year":2023,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Qinglan Project of Jiangsu Province of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Arable land; Environmental science; Water use; Nexus (standard); Rainwater harvesting; Irrigation; Water resources; Agriculture; Water resource management; Agricultural economics; Geography; Agronomy; Economics","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":[0.0008267075,0.0001012351,0.0002244817,0.00002839107,0.000333219,0.0000150193,0.0003303054,0.00005006543,0.00004841334],"category_scores_gemma":[0.0002153273,0.00004132709,0.0001130379,0.0002030818,0.0005058278,0.0001849959,0.0002783669,0.0002455404,0.000006144287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001333532,"about_ca_system_score_gemma":0.00001440029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008999515,"about_ca_topic_score_gemma":0.0005775309,"domain_scores_codex":[0.9985816,0.000383911,0.0003549646,0.0001438075,0.0002987173,0.0002370174],"domain_scores_gemma":[0.9991751,0.0001058443,0.0004457101,0.0002066485,0.00004398312,0.000022708],"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":[0.000259293,0.0001206776,0.07270282,0.000007372824,0.0003871318,0.00003606372,0.01002293,0.04500137,0.8680643,0.0005589221,0.002595349,0.0002437196],"study_design_scores_gemma":[0.0005427862,0.0001450132,0.8225175,0.00002984884,0.0001072886,0.0001770264,0.001354353,0.000575494,0.1156061,0.05829673,0.0005698325,0.00007798447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672766,0.0001713022,0.00003030796,0.03122905,0.000607443,0.00009407411,0.000005778808,0.000006371965,0.0005791197],"genre_scores_gemma":[0.9994905,0.00002784669,0.00001050801,0.00006248838,0.0003089377,0.000002625177,0.000001067706,0.000007674406,0.00008835536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7524582,"threshold_uncertainty_score":0.2562885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008914065505952659,"score_gpt":0.2412337666428666,"score_spread":0.232319701136914,"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."}}