{"id":"W3133749196","doi":"10.1029/2019ea000933","title":"Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling","year":2021,"lang":"en","type":"article","venue":"Earth and Space Science","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Forecast skill; Flood myth; Environmental science; Precipitation; Ensemble forecasting; Climatology; Ensemble average; Quantitative precipitation forecast; Computer science; Econometrics; Forecast verification; Meteorology; Statistics; Mathematics; Machine learning; Geography; Geology","routes":{"ca_aff":false,"ca_fund":false,"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":[0.001117437,0.0004870363,0.0004701727,0.0004111216,0.0002330136,0.0006285408,0.000398654,0.0004031094,0.0002609994],"category_scores_gemma":[0.001683809,0.0002475364,0.0004235273,0.0003248605,0.0001566949,0.0004177084,0.0003738938,0.0003957374,0.00005990044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007095902,"about_ca_system_score_gemma":0.001177498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04479476,"about_ca_topic_score_gemma":0.0291823,"domain_scores_codex":[0.999854,0.00004954017,0.000009601328,0.00002998494,0.00002285162,0.00003397804],"domain_scores_gemma":[0.9994416,0.0002854394,0.00006797448,0.00003930079,0.0001152199,0.00005044724],"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":[0.00006490693,0.00003281984,0.01453076,0.0000113906,0.0000533976,0.0000299598,0.00001336335,0.9737759,0.00173529,0.0001190805,0.0001154518,0.009517659],"study_design_scores_gemma":[0.00001057867,0.00001618112,0.003941851,0.000002433325,0.00000869759,0.000003354192,0.00001119612,0.9948305,0.0009971799,0.0001112191,0.00006130048,0.000005366551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783633,0.00012369,0.02054438,0.0001066641,0.00001744752,0.00001517618,0.000207521,0.0001640589,0.0004577157],"genre_scores_gemma":[0.9942773,0.00003211159,0.005443499,0.000008566795,0.000004785637,0.000006767186,0.0001211702,0.000008464704,0.00009736698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04479476,"threshold_uncertainty_score":0.08906806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04552974746974321,"score_gpt":0.2976550514800058,"score_spread":0.2521253040102626,"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."}}