{"id":"W4401442804","doi":"10.1038/s41598-024-69309-3","title":"Publisher Correction: Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm","year":2024,"lang":"en","type":"erratum","venue":"Scientific Reports","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Convolutional neural network; Long short term memory; Computer science; Feature selection; Feature (linguistics); Artificial intelligence; Selection (genetic algorithm); Term (time); Artificial neural network; Machine learning; Pattern recognition (psychology); Data mining; Algorithm; Recurrent 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001833342,0.002309781,0.001558867,0.003416901,0.002182609,0.002841645,0.002786168,0.003233071,0.04333425],"category_scores_gemma":[0.03141808,0.0008832057,0.001283555,0.00338773,0.001427149,0.002153612,0.001602361,0.007129653,0.03482465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002283572,"about_ca_system_score_gemma":0.003165207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02475683,"about_ca_topic_score_gemma":0.02605275,"domain_scores_codex":[0.9978113,0.000268942,0.0004418638,0.0003597068,0.0009831967,0.0001350012],"domain_scores_gemma":[0.9824674,0.002750515,0.0007667119,0.001351174,0.01214624,0.0005180594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003674712,0.000007402637,0.0001562164,0.0001472438,0.0000143386,0.0003908678,0.00004816637,0.0002029677,0.000101172,0.001149665,0.9859903,0.011755],"study_design_scores_gemma":[0.00002302586,0.00001792001,0.0007290221,0.0001913576,0.00003408568,0.0006873268,0.00009443553,0.0008049658,0.0006588584,0.001498213,0.9952272,0.00003355186],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0003419538,0.001012159,0.002321085,0.01853707,0.9717856,0.00001897142,0.001731817,0.0006284796,0.003622829],"genre_scores_gemma":[0.0405122,0.01186589,0.01852592,0.03827672,0.3133499,0.0002125462,0.008685161,0.004311292,0.5642603],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04333425,"threshold_uncertainty_score":0.1449675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224562434826658,"score_gpt":0.2402426693969713,"score_spread":0.2179970450487048,"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."}}