{"id":"W4408673594","doi":"10.1016/j.jclepro.2025.145348","title":"Optimizing agricultural water-land resource allocation in water-economic-environment cycles considering uncertainties of spatiotemporal water footprints","year":2025,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Water resources management and optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Agriculture; Resource (disambiguation); Environmental science; Farm water; Natural resource economics; Water resources; Water resource management; Water use; Agricultural land; Environmental resource management; Environmental engineering; Water conservation; Economics; Computer science; Geography; 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":[0.0004371079,0.0001396965,0.0002293177,0.0003212042,0.00004628031,0.00004967179,0.0001081533,0.00005738019,0.00002203224],"category_scores_gemma":[0.00000528738,0.00008432738,0.00006069772,0.00003382597,0.00003693379,0.0003572247,0.00005602089,0.0001424485,0.000005395386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001869025,"about_ca_system_score_gemma":0.000003352983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003000141,"about_ca_topic_score_gemma":0.00001583977,"domain_scores_codex":[0.9988581,0.00004790969,0.0006214133,0.0001398074,0.0001346446,0.0001981112],"domain_scores_gemma":[0.9997169,0.000006512643,0.00008927503,0.0001267879,0.00003589106,0.00002464111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005637973,0.00002270176,0.002268138,0.0001345288,0.00006531384,0.000001800301,0.00247581,0.9447919,0.04848487,0.000007474545,0.0001347195,0.00155637],"study_design_scores_gemma":[0.0006236375,0.00005204515,0.003366967,0.0001662617,0.0000547132,0.00001231624,0.001082289,0.01193853,0.9727817,0.0002069495,0.009534183,0.0001804149],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957637,0.00006519846,0.001992832,0.0009877015,0.0004252542,0.0001732531,2.669338e-7,0.00002657193,0.0005652535],"genre_scores_gemma":[0.9983719,0.00008108639,0.0009483444,0.000007562448,0.0001642897,0.000003835572,0.00002495387,0.00001496733,0.0003830655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9328533,"threshold_uncertainty_score":0.3438771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008573135325441749,"score_gpt":0.1878979355301507,"score_spread":0.179324800204709,"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."}}