{"id":"W1987213677","doi":"10.1016/j.jhydrol.2013.10.052","title":"Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models","year":2013,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":468,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Support vector machine; Autoregressive integrated moving average; Wavelet; Computer science; Term (time); Mean squared error; Regression; Data mining; Artificial intelligence; Machine learning; Statistics; Mathematics; Time series","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":[0.001348018,0.000188822,0.0004443854,0.0001324705,0.0001336974,0.00002742578,0.0003357776,0.0002393498,0.0006609909],"category_scores_gemma":[0.00005822011,0.000122378,0.0001045232,0.0003464035,0.0003290689,0.0006117094,0.0001730529,0.0007645713,0.00001773634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008923889,"about_ca_system_score_gemma":0.00001979584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004093334,"about_ca_topic_score_gemma":0.0007074412,"domain_scores_codex":[0.9978229,0.0005189314,0.000637359,0.0002474039,0.0002601415,0.00051327],"domain_scores_gemma":[0.9990678,0.0002356245,0.0004059492,0.0001855352,0.00001451006,0.00009054577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001079462,0.00009300368,0.8751687,0.000007705816,0.00002952615,0.0008740636,0.001950351,0.1171035,0.0006940656,0.00002699412,0.0006524819,0.003291661],"study_design_scores_gemma":[0.0008648306,0.0002569609,0.428623,0.00003672881,0.00005684505,0.001658844,0.00003530397,0.5633323,0.0000395764,0.004870708,0.00006048041,0.0001644743],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969119,0.0001149894,0.0003567857,0.00192426,0.0001431958,0.0001348987,5.890689e-7,0.000003519531,0.0004098886],"genre_scores_gemma":[0.9969549,0.00005175011,0.001325119,0.001437499,0.0001725937,0.000003406046,0.000002071151,0.00001206813,0.00004059072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4465457,"threshold_uncertainty_score":0.7237388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02596509696072136,"score_gpt":0.2554579024287879,"score_spread":0.2294928054680666,"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."}}