{"id":"W4388976461","doi":"10.1007/s11356-023-30774-4","title":"Forecasting water quality variable using deep learning and weighted averaging ensemble models","year":2023,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Computer science; Recurrent neural network; Artificial intelligence; Convolutional neural network; Ensemble forecasting; Water quality; Artificial neural network; Genetic algorithm; Deep learning; Machine learning; Predictive modelling; Ecology; Biology","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":[0.0008907469,0.0006323758,0.001025789,0.0007231333,0.0002913712,0.0005944638,0.0007374827,0.000641425,0.0006405642],"category_scores_gemma":[0.001882646,0.0003483101,0.0007771511,0.001096128,0.000228268,0.001320987,0.0005581042,0.001248859,0.0001348235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005300572,"about_ca_system_score_gemma":0.0006623729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01968653,"about_ca_topic_score_gemma":0.02017613,"domain_scores_codex":[0.9997641,0.00004477019,0.00001922832,0.00006330364,0.00006466088,0.00004390129],"domain_scores_gemma":[0.9993263,0.00027739,0.00007346453,0.00007061255,0.0002181054,0.0000340994],"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":[0.0000631181,0.00008125242,0.003547897,0.00001642035,0.0001221905,0.00002189239,0.00001536348,0.9425968,0.001198932,0.0009593483,0.0007896115,0.05058715],"study_design_scores_gemma":[7.965028e-7,0.000002426477,0.0001501783,3.837901e-7,0.000003272792,8.040309e-7,6.184841e-7,0.9995447,0.00007240644,0.0002064524,0.00001696566,9.665408e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3979946,0.0008322057,0.5967554,0.0004764406,0.000305798,0.00002809262,0.0004482422,0.0007172902,0.002442046],"genre_scores_gemma":[0.963003,0.0002408744,0.03484153,0.00006471242,0.00009310912,0.00002429995,0.0004362171,0.00002456754,0.001271771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01968653,"threshold_uncertainty_score":0.03914386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1166973988005425,"score_gpt":0.3441548242888416,"score_spread":0.2274574254882991,"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."}}