{"id":"W4407218933","doi":"10.1088/2976-601x/adabe9","title":"The iGains4Gains model guides irrigation water conservation and allocation to enhance nexus gains across water, food, carbon emissions, and nature","year":2025,"lang":"en","type":"article","venue":"Environmental Research Food Systems","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"Consortium of International Agricultural Research Centers; Wellcome Trust","keywords":"Nexus (standard); Water conservation; Irrigation; Greenhouse gas; Environmental science; Irrigation management; Water resource management; Water use; Water resources; Production (economics); Environmental economics; Natural resource economics; Computer science; Economics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005202261,0.0005883743,0.0005080599,0.0005184077,0.0004951799,0.001809377,0.001512534,0.001148591,0.01097304],"category_scores_gemma":[0.001177338,0.0004090768,0.0007779588,0.0005643737,0.0007275493,0.001158118,0.001082792,0.0008888079,0.0009238613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001769481,"about_ca_system_score_gemma":0.002186387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01830867,"about_ca_topic_score_gemma":0.02435717,"domain_scores_codex":[0.9998319,0.00005016023,0.000007676089,0.0000339021,0.00003835151,0.00003799924],"domain_scores_gemma":[0.9995587,0.00023125,0.00004671016,0.00003592054,0.00008606336,0.00004131729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004321914,0.00003235073,0.001628538,0.00003015045,0.00001220006,0.00005126305,0.00005599307,0.9711983,0.0004575344,0.01918962,0.001457729,0.005843118],"study_design_scores_gemma":[0.00002121584,0.00002422674,0.0002707258,0.00001051123,0.000007745842,0.00001450018,0.00005014356,0.9867505,0.0003971677,0.007134933,0.005310258,0.00000810736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2917665,0.0002198667,0.5291643,0.00243502,0.0001898951,0.0004383519,0.00616917,0.003869956,0.165747],"genre_scores_gemma":[0.876866,0.0002937969,0.09241733,0.0002273064,0.00002909846,0.0003450126,0.001639011,0.0003242902,0.0278582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01830867,"threshold_uncertainty_score":0.03670847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03289780057479592,"score_gpt":0.3343536748144622,"score_spread":0.3014558742396663,"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."}}