{"id":"W811339590","doi":"10.25165/ijabe.v1i2.2","title":"Evaluation of regional water security using water poverty index.","year":2008,"lang":"en","type":"article","venue":"International journal of agricultural and biological engineering","topic":"Water Resources and Sustainability","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Environment Research Council; National Natural Science Foundation of China","keywords":"Grading (engineering); Index (typography); Poverty; Index method; Water resources; Environmental science; Water security; Water resource management; Mathematics; Business; Engineering; Computer science; Civil engineering; Economics; Economic growth","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":[0.000417432,0.00007387996,0.0001086259,0.00002118141,0.0000301639,0.000009326342,0.000126981,0.00004895758,0.000203235],"category_scores_gemma":[0.00003412648,0.00002758748,0.00006996257,0.00002455199,0.00005940928,0.0001512178,0.0001086624,0.00009402751,0.000001446272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001205137,"about_ca_system_score_gemma":0.000003113515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005458303,"about_ca_topic_score_gemma":0.000001122341,"domain_scores_codex":[0.9990409,0.00003548016,0.0002316649,0.00008296569,0.0004996898,0.0001093146],"domain_scores_gemma":[0.9996965,0.00001243547,0.00005180852,0.00002467551,0.000166882,0.00004771538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001192327,0.0001877967,0.1708362,0.000009246394,0.0001638519,0.00003326938,0.002678572,0.2285282,0.5956818,0.00002447994,0.0001529849,0.001584288],"study_design_scores_gemma":[0.0005477561,0.0001176618,0.9608909,0.00001709112,0.000021304,0.0008617588,0.0001256538,0.009360918,0.02614456,0.0004915381,0.001288357,0.0001325052],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993831,0.00003521787,0.00008110255,0.0003208446,0.00009317486,0.00004295608,0.000001246423,0.000003644654,0.00003873889],"genre_scores_gemma":[0.9997135,0.00001890794,0.0001247731,0.00003062004,0.00009875339,5.62186e-7,0.00000370263,0.000001310355,0.000007850602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7900547,"threshold_uncertainty_score":0.2225281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02716803910285627,"score_gpt":0.224481615573476,"score_spread":0.1973135764706198,"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."}}