{"id":"W3042922613","doi":"10.5194/egusphere-egu2020-4233","title":"Using a boundary-corrected wavelet transform coupled with machine learning and hybrid deep learning approaches for multi-step water level forecasting in Lakes Michigan and Ontario","year":2020,"lang":"en","type":"article","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; McGill University","funders":"","keywords":"Artificial intelligence; Discrete wavelet transform; Support vector machine; Wavelet; Convolutional neural network; Computer science; Deep learning; Machine learning; Wavelet transform","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001678565,0.0003166442,0.0001733511,0.0002816358,0.0004666167,0.0006223364,0.0005877499,0.0004301996,0.00135858],"category_scores_gemma":[0.0008710378,0.0001558882,0.0003036941,0.0005696521,0.0002236429,0.0005186885,0.0003780694,0.0003924355,0.0001878506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002819055,"about_ca_system_score_gemma":0.002899531,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7256166,"about_ca_topic_score_gemma":0.8019373,"domain_scores_codex":[0.9999108,0.000008090765,0.000004855647,0.00002314,0.00002937341,0.00002377257],"domain_scores_gemma":[0.999878,0.00002402416,0.00001812519,0.00001089494,0.00005369481,0.00001527507],"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":[0.000329349,0.00009638527,0.1033739,0.0001624868,0.0001246138,0.0006758589,0.0005266994,0.6024911,0.02426247,0.00443924,0.0107962,0.2527216],"study_design_scores_gemma":[0.000009517756,0.00001028383,0.02483541,0.000009607603,0.00001321431,0.00001420989,0.0001374977,0.9699229,0.002528717,0.0005794928,0.001923579,0.00001558811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9189072,0.0004917328,0.06370407,0.001522634,0.00007393702,0.00003452974,0.002126686,0.0006437321,0.01249549],"genre_scores_gemma":[0.9829146,0.0001263364,0.01280994,0.00003717879,0.00001261166,0.00001365833,0.0009129355,0.00003085815,0.003141922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7256166,"threshold_uncertainty_score":0.5519985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1196018800088413,"score_gpt":0.2415624210213082,"score_spread":0.1219605410124669,"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."}}