{"id":"W2944941154","doi":"10.3808/jeil.201900005","title":"Ensemble Learning Enhanced Stepwise Cluster Analysis for River Ice Breakup Date Forecasting","year":2019,"lang":"en","type":"article","venue":"Journal of Environmental Informatics Letters","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Alberta Environment and Parks","keywords":"Breakup; Statistic; Stepwise regression; Statistics; Multivariate statistics; Flooding (psychology); Environmental science; Computer science; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001204647,0.0009606512,0.001021744,0.001280528,0.0005293268,0.000540042,0.000910465,0.0004407018,0.0007615145],"category_scores_gemma":[0.002317914,0.0003402703,0.001352897,0.001280276,0.0001848001,0.0005899086,0.0006218667,0.001010352,0.0002252443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005621551,"about_ca_system_score_gemma":0.00125871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03342159,"about_ca_topic_score_gemma":0.02287288,"domain_scores_codex":[0.999563,0.0001244313,0.00003023646,0.00009035897,0.000117517,0.00007445786],"domain_scores_gemma":[0.9989797,0.0004237716,0.00008309648,0.00008256911,0.0003821446,0.00004855737],"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":[0.0001280688,0.00008172196,0.005947081,0.00003866765,0.0001786068,0.00008213169,0.00006457407,0.8833978,0.001750789,0.0007758609,0.001848754,0.1057058],"study_design_scores_gemma":[0.000001409131,0.000007749717,0.000580256,0.000001588704,0.000007551606,0.000003077824,0.00000543747,0.9988587,0.0002165526,0.0002018498,0.000111805,0.000003965294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2451663,0.001192164,0.7476875,0.0003636215,0.0001877244,0.0001272301,0.0007663557,0.002147807,0.002361302],"genre_scores_gemma":[0.9203931,0.0004001268,0.07615124,0.00005770877,0.0000695514,0.0001230176,0.001315973,0.0000822954,0.00140708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03342159,"threshold_uncertainty_score":0.06645405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00812113394588126,"score_gpt":0.1840153522543846,"score_spread":0.1758942183085034,"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."}}