{"id":"W4416510098","doi":"10.1016/j.agwat.2025.109960","title":"Assessing the real impact of inter-provincial grain trade on water and land resources within China via a modified framework","year":2025,"lang":"en","type":"article","venue":"Agricultural Water Management","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Virtual water; Arable land; Resource (disambiguation); Agricultural productivity; Irrigation; Water resources; Land management; Agriculture; Agricultural land","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":[0.000342314,0.0002112796,0.0001759344,0.000028572,0.000226354,0.0001315247,0.0002386577,0.00006542086,0.00009752627],"category_scores_gemma":[0.000004656879,0.00007505427,0.00010317,0.0000906567,0.0002422481,0.0002412452,0.0004817246,0.0001819517,0.000009687825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002868848,"about_ca_system_score_gemma":9.939905e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001376997,"about_ca_topic_score_gemma":0.0000703222,"domain_scores_codex":[0.9987661,0.0001247222,0.0002517571,0.0003031539,0.000217685,0.0003365717],"domain_scores_gemma":[0.9996365,0.00002111642,0.00004601418,0.0002371165,0.000001825206,0.00005743075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007831929,0.001487985,0.7439497,0.0005022449,0.00104139,0.0001037879,0.08189605,0.02171626,0.09798712,0.001958766,0.003156268,0.04541723],"study_design_scores_gemma":[0.000259679,0.0001478801,0.98665,0.00003565669,0.00004877263,0.000001897092,0.001290867,0.0002269887,0.006179971,0.004909241,0.0001001201,0.000148926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864236,0.000004927508,0.0001491376,0.001514363,0.00006638878,0.000472458,0.000002541462,0.00002302301,0.0113436],"genre_scores_gemma":[0.9985698,0.000005728712,0.0001050738,0.0001228802,0.00002250882,0.0000214424,0.00001648409,0.000005876765,0.001130142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2427003,"threshold_uncertainty_score":0.3060624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005682304381118982,"score_gpt":0.2461729399768924,"score_spread":0.2404906355957734,"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."}}