{"id":"W4307313889","doi":"10.1016/j.wasec.2022.100126","title":"Elevating the role of water resilience in food system dialogues","year":2022,"lang":"en","type":"article","venue":"Water Security","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Monash University Malaysia; Sveriges Lantbruksuniversitet; Monash University; Nature Conservancy; Water for Food Daugherty Global Institute","keywords":"Corporate governance; Business; Resilience (materials science); Preparedness; Food systems; Sustainability; Equity (law); Environmental resource management; Environmental economics; Knowledge management; Food security; Agriculture; Political science; Computer science; Ecology; Economics","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":[0.0006027362,0.0001289627,0.0001742207,0.00003184823,0.0003508482,0.00001239186,0.0004783429,0.00002518362,0.0001808339],"category_scores_gemma":[0.000007892429,0.00006781924,0.00004759262,0.0001225727,0.0001596576,0.0001176054,0.001646065,0.0001636337,0.00004490571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001787495,"about_ca_system_score_gemma":0.000002739707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002329598,"about_ca_topic_score_gemma":0.001363448,"domain_scores_codex":[0.9983345,0.0002591595,0.0002834045,0.0002971278,0.0004012933,0.0004244921],"domain_scores_gemma":[0.9995742,0.00003165669,0.00004431047,0.0003145904,0.00000574771,0.00002943342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001934139,0.0005944566,0.1356685,0.0001510592,0.00009819981,0.00006201799,0.2190646,0.005924397,0.6115424,0.02400057,0.001124395,0.001575996],"study_design_scores_gemma":[0.0003197895,0.0002928701,0.005980976,0.0000160358,0.00001065301,0.00001751382,0.01062505,0.001345805,0.9188748,0.05306285,0.009176943,0.0002767387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804183,0.00009570125,0.000003853901,0.0002754448,0.0001214829,0.0001495903,0.00002549331,0.0000346511,0.01887551],"genre_scores_gemma":[0.9996933,8.697564e-7,0.00001689461,0.00005856289,0.00002494945,0.0001073285,0.000008355599,0.00001105611,0.00007869106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3073324,"threshold_uncertainty_score":0.3521671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00787771075709273,"score_gpt":0.1756661788325073,"score_spread":0.1677884680754145,"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."}}