{"id":"W2161982132","doi":"10.1080/07055900.2013.777896","title":"Water Supply, Demand, and Quality Indicators for Assessing the Spatial Distribution of Water Resource Vulnerability in the Columbia River Basin","year":2013,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Water resources management and optimization","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vulnerability (computing); Water resource management; Water supply; Drainage basin; Environmental science; Water quality; Water resources; Resource (disambiguation); Vulnerability assessment; Structural basin; Hydrology (agriculture); Geography; Environmental engineering; Ecology; Computer science; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009563675,0.0001266765,0.0001725135,0.000005084927,0.000159674,0.0002073684,0.0001998531,0.00007475512,0.00009905067],"category_scores_gemma":[0.0000235346,0.0000702382,0.00005190715,0.0000905867,0.000129077,0.0002209655,0.00006985015,0.0001264588,0.000004030597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003824018,"about_ca_system_score_gemma":0.000002323061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002328847,"about_ca_topic_score_gemma":0.0003079126,"domain_scores_codex":[0.9988429,0.0002030557,0.000332551,0.0001769669,0.000169376,0.0002751429],"domain_scores_gemma":[0.9995276,0.000108704,0.00004166682,0.0002641694,0.00002861821,0.00002923796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002951984,0.00009370285,0.937058,0.0003299456,0.00007535299,8.730889e-7,0.01055068,0.03461804,0.0002746448,0.00009262961,0.007106611,0.009769951],"study_design_scores_gemma":[0.0009989298,0.00004517382,0.9096959,0.00003092657,0.00006101709,9.102513e-7,0.001246513,0.06267543,0.004318068,0.0009440401,0.01970594,0.0002771254],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898797,0.0000225282,0.008630054,0.0003530642,0.00004594713,0.000750863,0.00001210138,0.00004146398,0.0002642243],"genre_scores_gemma":[0.9992957,0.000004234591,0.0002254966,0.00006018274,0.00004875263,0.00002713412,0.0002101442,0.00001868558,0.0001097032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02805739,"threshold_uncertainty_score":0.3520535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008687169652254844,"score_gpt":0.2192156355469369,"score_spread":0.210528465894682,"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."}}