{"id":"W4398776308","doi":"10.1007/s11625-024-01509-2","title":"A multi-case institutional analysis of water–energy–food nexus governance","year":2024,"lang":"en","type":"article","venue":"Sustainability Science","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nexus (standard); Corporate governance; Landscape ecology; Water energy; Sustainable development; Business; Environmental planning; Political science; Environmental resource management; Natural resource economics; Environmental science; Ecology; Economics; Biology; Computer science","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001031603,0.0002262323,0.0003077295,0.0002352516,0.0005913223,0.00009574663,0.0006559854,0.00005618137,0.0002885035],"category_scores_gemma":[0.0003731683,0.0001636305,0.0001967144,0.004314144,0.004211592,0.001034984,0.001095621,0.0001143197,0.00002007539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001783043,"about_ca_system_score_gemma":0.0002443056,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006862347,"about_ca_topic_score_gemma":0.003922569,"domain_scores_codex":[0.9968836,0.00006969934,0.0004062702,0.001013167,0.0009110573,0.0007162595],"domain_scores_gemma":[0.9989113,0.00008139621,0.0000607045,0.0006358742,0.0001334227,0.0001773313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000840769,0.001292107,0.01749005,0.0003651603,0.0007448594,0.002299425,0.01115777,0.2296518,0.02369598,0.6939365,0.0003560158,0.01892629],"study_design_scores_gemma":[0.0007108715,0.000709682,0.1056606,0.00005369596,0.0008727486,0.000363714,0.005471223,0.5805399,0.044902,0.2429545,0.01630524,0.001455751],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973824,0.0003632473,0.01318213,0.001453088,0.0004139865,0.0002410991,0.000157977,0.0001573839,0.01020709],"genre_scores_gemma":[0.9984269,0.000003362829,0.0006768616,0.00006458012,0.00001819505,0.00004333372,0.000004589266,0.000008167336,0.0007539891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4509819,"threshold_uncertainty_score":0.999751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045031932266771,"score_gpt":0.2459675362700206,"score_spread":0.2355172169473529,"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."}}