{"id":"W2073105236","doi":"10.1007/s11269-012-0193-z","title":"Analytical Support for Integrated Water Resources Management: A New Method for Addressing Spatial and Temporal Variability","year":2012,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"System dynamics; Integrated water resources management; Watershed; Computer science; Process (computing); Decision support system; Hydrogeology; Environmental resource management; Function (biology); Water resources; Environmental science; Operations research; Engineering; Data mining; Ecology; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001672458,0.0005575911,0.000515243,0.0004708385,0.0002903919,0.000447162,0.0004180684,0.0001587407,0.0002457192],"category_scores_gemma":[0.00001056175,0.0003888313,0.0002053351,0.0001683967,0.00007518197,0.0004146732,0.0004569316,0.0001605406,0.00003696404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001228126,"about_ca_system_score_gemma":0.00000153201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008222915,"about_ca_topic_score_gemma":0.00001373667,"domain_scores_codex":[0.996832,0.0001189646,0.0007042158,0.0006657569,0.0003739162,0.001305138],"domain_scores_gemma":[0.9989668,0.00006091699,0.00006685929,0.00053444,0.00004750626,0.0003234471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005109707,0.001687401,0.03595455,0.02657069,0.01200581,0.0001064076,0.09572496,0.12725,0.001703099,0.008507683,0.08538687,0.5999929],"study_design_scores_gemma":[0.002139602,0.0001168271,0.0008363355,0.00007941121,0.0007012109,0.000003380689,0.0005000277,0.1655153,0.002043653,0.0008489513,0.8265432,0.0006720781],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0740876,0.00009414096,0.905125,0.0004498211,0.0005128083,0.00346847,0.00003795885,0.0007266566,0.01549749],"genre_scores_gemma":[0.8119135,0.00003794193,0.1612441,0.0003296074,0.0009515157,0.0007467552,0.001042059,0.0002660056,0.02346847],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7438809,"threshold_uncertainty_score":0.9998564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02269163887589416,"score_gpt":0.2621119044712558,"score_spread":0.2394202655953616,"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."}}