{"id":"W2883578007","doi":"10.1016/j.scitotenv.2018.07.001","title":"River ice breakup timing prediction through stacking multi-type model trees","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Sun Yat-sen University; National Natural Science Foundation of China; Alberta Environment and Parks","keywords":"Breakup; Environmental science; Ensemble forecasting; Hydrology (agriculture); Climatology; Meteorology; Geology; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0003291295,0.0004761397,0.0006359693,0.0006760393,0.0003935666,0.0007083153,0.0007690862,0.0007538799,0.001768039],"category_scores_gemma":[0.001273455,0.0004254594,0.0009254761,0.0008173742,0.0001710707,0.001199316,0.0002908519,0.0006427308,0.0004069054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004390156,"about_ca_system_score_gemma":0.0006922767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01716313,"about_ca_topic_score_gemma":0.02077438,"domain_scores_codex":[0.9999098,0.00001583258,0.000006736673,0.00003558908,0.00001128525,0.00002073518],"domain_scores_gemma":[0.9995424,0.000194498,0.00005216249,0.00006121019,0.00009331607,0.0000564499],"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":[0.00005981336,0.00002938566,0.005528662,0.000007289934,0.00003166141,0.00002779376,0.000007207327,0.9836638,0.0004310278,0.0002010869,0.0003123362,0.009699957],"study_design_scores_gemma":[0.000002773469,0.000005446215,0.0006557349,0.000001074741,0.000007508856,0.000002523303,0.000003257298,0.998914,0.0000992295,0.0002572414,0.0000484863,0.000002694846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8861002,0.000300306,0.10686,0.0001848761,0.00008795968,0.00002761973,0.002042287,0.0009513164,0.003445361],"genre_scores_gemma":[0.9870257,0.00007654686,0.01090413,0.00002220408,0.00001683463,0.0000160845,0.001193616,0.0000473795,0.0006975061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01716313,"threshold_uncertainty_score":0.03412646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02411928079669456,"score_gpt":0.2180545900731046,"score_spread":0.1939353092764101,"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."}}