{"id":"W4408539295","doi":"10.1073/pnas.2410521122","title":"Agriculture’s impact on water–energy balance varies across climates","year":2025,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Temperate climate; Precipitation; Algorithm; Agriculture; Database; Environmental science; Water balance; Physical geography; Mathematics; Physics; Geography; Geology; Ecology; Meteorology; Computer science; Archaeology; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005898412,0.000103587,0.0002239113,0.0007169864,0.0002282909,0.0005629003,0.0001508734,0.0001811866,0.0008928338],"category_scores_gemma":[0.0023733,0.0000956484,0.00036794,0.00182418,0.000573724,0.0004111789,0.0005272334,0.0002230888,0.0001433461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005367387,"about_ca_system_score_gemma":0.0003431035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01801738,"about_ca_topic_score_gemma":0.02090024,"domain_scores_codex":[0.9996908,0.0000881968,0.00002355519,0.00007904727,0.00006089681,0.00005748673],"domain_scores_gemma":[0.9986958,0.0005852731,0.0003917314,0.0001066825,0.0001511875,0.0000693876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003484171,0.00001304704,0.9860006,0.00002007394,0.00009354576,0.00005467587,0.0001538255,0.003596017,0.001240139,0.0004167665,0.0002483163,0.00812811],"study_design_scores_gemma":[0.000001403131,0.000005320089,0.9973044,0.000003489243,0.00001162744,0.00001412389,0.0001101921,0.001846987,0.000105752,0.0002012071,0.0003924528,0.000003022064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977586,0.0001600248,0.0005441751,0.00009859123,0.000002991125,0.000003243354,0.0004008703,0.00001430857,0.001017208],"genre_scores_gemma":[0.9992204,0.0001426199,0.0001980191,0.0000154147,0.000005701597,0.00000325775,0.0003306072,0.000003665597,0.00008030904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01801738,"threshold_uncertainty_score":0.03582501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346164498990358,"score_gpt":0.2851902414535193,"score_spread":0.2717285964636157,"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."}}