{"id":"W2057931119","doi":"10.2166/hydro.2009.018","title":"A new method for spatial and temporal analysis of risk in water resources management","year":2009,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water resources management and optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ambiguity; Water resources; CLARITY; Ignorance; Risk management; Confusion; Risk analysis (engineering); Spatial variability; Temporal scales; Computer science; Environmental resource management; Environmental science; Business; Psychology; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004260571,0.0000815479,0.0002689831,0.0007347186,0.00001513062,0.00003445598,0.00009536181,0.00002820518,0.0000100949],"category_scores_gemma":[0.000007391084,0.00005842072,0.00009558287,0.0002122104,0.000004643503,0.0002129276,0.00001581452,0.00006857557,2.81771e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001929863,"about_ca_system_score_gemma":0.000001367248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001513971,"about_ca_topic_score_gemma":0.00002482955,"domain_scores_codex":[0.9991012,0.00001106899,0.0005928742,0.00003056991,0.0001489583,0.0001152919],"domain_scores_gemma":[0.9996647,0.00002356475,0.000178276,0.00007517732,0.00001696432,0.0000412771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005057673,0.00001178427,0.001700513,0.00007933708,0.0004100203,0.00000258056,0.005715922,0.9671504,0.00001654296,0.00002354612,0.0002792617,0.02455948],"study_design_scores_gemma":[0.0007451923,0.00009005342,0.005206198,0.00002920522,0.0006572118,0.000001993615,0.0002519572,0.989737,0.0003075743,0.0005186687,0.002380047,0.00007494311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5085953,0.00005576489,0.4901915,0.00004259959,0.00003502934,0.0001155773,0.000002245792,0.000009615901,0.00095239],"genre_scores_gemma":[0.8066795,0.0001060872,0.1930977,0.00001834238,0.00003299245,4.540022e-7,0.000007747099,0.000005759458,0.00005137668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2980843,"threshold_uncertainty_score":0.2382328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005608787089017911,"score_gpt":0.2228346850854802,"score_spread":0.2172258979964623,"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."}}