{"id":"W2398936495","doi":"10.1007/s00477-016-1265-z","title":"Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model","year":2016,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":226,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Extreme learning machine; Computational intelligence; Wavelet; Index (typography); Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Artificial neural network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002103369,0.0003716173,0.0003327209,0.0001335551,0.001247226,0.00008632368,0.000305091,0.0001654503,0.0007095308],"category_scores_gemma":[0.0004253535,0.0002480421,0.00008257533,0.0002439239,0.001367266,0.0003367672,0.001311411,0.0009958094,0.0001051913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373723,"about_ca_system_score_gemma":0.00002985447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000475554,"about_ca_topic_score_gemma":0.00006889229,"domain_scores_codex":[0.9955289,0.0005282172,0.000378343,0.0009959476,0.001354194,0.001214427],"domain_scores_gemma":[0.9980953,0.0009102285,0.0001754065,0.0003319843,0.000009907039,0.000477172],"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":[0.00032416,0.000693712,0.3012236,0.00002312461,0.0001161751,0.00009511125,0.0005084817,0.4079924,0.05277193,0.00009100295,0.00006532221,0.236095],"study_design_scores_gemma":[0.001101308,0.0008347444,0.02153604,0.00009787195,0.00002764071,0.00005299574,0.00006744868,0.9699187,0.0002140464,0.005689101,0.0001036595,0.0003564667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7719749,0.00006371283,0.2261953,0.00009766548,0.00003979723,0.0006304588,0.00004180063,0.00004624483,0.0009101409],"genre_scores_gemma":[0.989861,0.0001155039,0.008844857,0.00002181494,0.00006370051,0.00009726684,0.000008960717,0.00005738411,0.0009295717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5619263,"threshold_uncertainty_score":0.9999972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06390041391793945,"score_gpt":0.3264049352160453,"score_spread":0.2625045212981059,"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."}}