{"id":"W4415163728","doi":"10.1007/s00704-025-05819-y","title":"Overcoming hydrological forecasting challenges through augmented adaptive deep algorithms: a case study of the great lakes across Canada and the U.S","year":2025,"lang":"en","type":"article","venue":"Theoretical and Applied Climatology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Université Laval","funders":"","keywords":"Mean absolute percentage error; Robustness (evolution); Generalizability theory; Quantile; Mean squared error; Gradient boosting; Mean absolute error; Boosting (machine learning); Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003669999,0.0001510809,0.000329431,0.000004974124,0.0005617273,0.000007372747,0.0001595395,0.00006775495,0.00003661057],"category_scores_gemma":[0.00004113414,0.00007383885,0.00002359205,0.00008476579,0.003643654,0.00002162659,0.001045008,0.0001781335,6.575308e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001862583,"about_ca_system_score_gemma":0.000003409257,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01390011,"about_ca_topic_score_gemma":0.1461803,"domain_scores_codex":[0.998865,0.0002010402,0.0002180555,0.0003097003,0.00009999268,0.0003061736],"domain_scores_gemma":[0.9990289,0.0007068943,0.00006417329,0.0001709947,0.000004117641,0.00002493244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001005268,0.0002049641,0.03493697,0.00005051495,0.0003620361,0.0003266153,0.01871296,0.0003231456,0.00001756239,0.9294216,0.00004404515,0.01459433],"study_design_scores_gemma":[0.01715646,0.0008762588,0.01789167,0.00006423939,0.001070447,0.001958467,0.2031414,0.04684759,0.0005645179,0.7087025,0.0008880539,0.0008384224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874614,0.0001751412,0.0001585588,0.003191906,0.00004638618,0.0004624015,0.000002677219,0.00001129672,0.00849021],"genre_scores_gemma":[0.9992015,0.00008989537,0.00005631748,0.0005544186,0.000006859601,0.00007383702,2.621235e-7,0.000003826723,0.00001308755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2207191,"threshold_uncertainty_score":0.9990678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01699347525560702,"score_gpt":0.239945554135954,"score_spread":0.222952078880347,"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."}}