{"id":"W2045859675","doi":"10.1109/ifsa-nafips.2013.6608480","title":"Non-parametric interval forecast models from fuzzy clustering of Numerical Weather Predictions","year":2013,"lang":"en","type":"article","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Numerical weather prediction; Cluster analysis; Probabilistic forecasting; Computer science; Probabilistic logic; Weather forecasting; Data mining; Forecast skill; Parametric statistics; Interval (graph theory); Fuzzy logic; Weather prediction; Global Forecast System; Set (abstract data type); Fuzzy clustering; Machine learning; Artificial intelligence; Statistics; Meteorology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009313133,0.0001334376,0.0001936576,0.00004712896,0.00006387079,0.00002769793,0.0002628444,0.00009163166,0.009257196],"category_scores_gemma":[0.0000446505,0.0001009197,0.00008668633,0.0003707122,0.0001519169,0.0002887604,0.0003547455,0.0001425276,0.001127389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008386015,"about_ca_system_score_gemma":0.000003389641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006415827,"about_ca_topic_score_gemma":0.00004472424,"domain_scores_codex":[0.9988639,0.0000227667,0.0002882859,0.0003035283,0.0002494935,0.0002720198],"domain_scores_gemma":[0.999451,0.00008299868,0.00007558239,0.0002531427,0.000009584225,0.0001276959],"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":[0.00001220052,0.0002323639,0.02205409,0.000004309625,0.000028989,0.000001658669,0.000361398,0.950767,0.003361576,0.00002990893,0.003637217,0.01950933],"study_design_scores_gemma":[0.0001522752,0.0001602283,0.01321659,0.00001148291,0.00001082885,0.000003696392,0.00001923853,0.9815804,0.0002673905,0.004356652,0.0001076084,0.0001136076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.764475,0.000003079594,0.1798895,0.0001159293,0.0001028734,0.0001673421,0.00000640543,0.00007252834,0.05516737],"genre_scores_gemma":[0.9749672,0.000001412,0.02399498,0.0001449741,0.00003271788,0.00002521288,0.000003978882,0.00001565344,0.0008138117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2104923,"threshold_uncertainty_score":0.9996504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517564184868838,"score_gpt":0.2273286863428888,"score_spread":0.2021530444942004,"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."}}