{"id":"W2778094897","doi":"10.1002/hyp.11429","title":"Multiple model combination methods for annual maximum water level prediction during river ice breakup","year":2017,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas","funders":"Alberta Environment and Parks","keywords":"Breakup; Mean squared error; Multivariate statistics; Cross-validation; Environmental science; Statistics; Computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003873013,0.0001525115,0.0001792988,0.00003418748,0.001092708,0.00008834233,0.0003308131,0.0001448044,0.0001142862],"category_scores_gemma":[0.0005631899,0.00009754636,0.0000536035,0.00003085033,0.0002515062,0.0006857962,0.00004576686,0.0001387226,0.00003911102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006387316,"about_ca_system_score_gemma":0.00002938731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002070054,"about_ca_topic_score_gemma":0.00009897531,"domain_scores_codex":[0.9989141,0.00004246915,0.0001971222,0.0003372102,0.0001453683,0.0003636818],"domain_scores_gemma":[0.9992503,0.0002097067,0.0001068805,0.0001938731,0.0001487594,0.00009051453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001926561,0.0005234219,0.7259668,0.001370123,0.0001623995,0.00002672466,0.007378806,0.1392121,0.002074221,0.0002289507,0.000247742,0.1208822],"study_design_scores_gemma":[0.0008639874,0.0002055114,0.1680519,0.00001322479,0.00003585621,0.00002005331,0.00008939797,0.7968486,0.001020715,0.0321374,0.0005199271,0.0001934113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8344139,0.00002670946,0.1635337,0.0006045898,0.0001668693,0.0002624437,0.0003147205,0.00007836751,0.0005986607],"genre_scores_gemma":[0.9675937,0.00004370806,0.03149483,0.0001558384,0.00009576499,0.00001093658,0.0002187171,0.00000497478,0.0003814925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6576365,"threshold_uncertainty_score":0.8404338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04388235654743582,"score_gpt":0.284665139229489,"score_spread":0.2407827826820532,"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."}}