{"id":"W4224301794","doi":"10.21203/rs.3.rs-1552614/v1","title":"A Sustainable Climate Forecast System for Post-processing of Precipitation With Application of Machine Learning Computations","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Flood myth; Random forest; Decision support system; Task (project management); Sustainable development; Precipitation; Computation; Machine learning; Data processing; Data mining; Meteorology; Systems engineering; Engineering; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.001341684,0.0001771788,0.000338491,0.0005319533,0.0003387054,0.00005455372,0.0002402299,0.0001147787,0.000008524163],"category_scores_gemma":[0.0001269483,0.0001772984,0.00008761048,0.0005521395,0.00005968957,0.0001066586,0.0002827337,0.0006899249,5.114753e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003729635,"about_ca_system_score_gemma":0.0002009271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004371816,"about_ca_topic_score_gemma":0.00007833482,"domain_scores_codex":[0.9981031,0.0001411515,0.0004292022,0.0002823653,0.0005445019,0.0004996834],"domain_scores_gemma":[0.9977297,0.0003949298,0.0002180797,0.0002410511,0.001352357,0.00006391633],"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":[0.0001090973,0.00003033285,0.001279673,0.03730097,0.00005082221,0.000002146455,0.002332335,0.9472684,0.0004717208,0.00331079,0.000007831715,0.007835879],"study_design_scores_gemma":[0.000371723,0.0003638854,0.0004319045,0.001431575,0.00003102266,0.000003885866,0.01025438,0.9848475,0.001230467,0.0001648836,0.0006902455,0.0001784848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5553551,0.003774158,0.4261443,0.00007723884,0.0001942686,0.005009446,0.001224543,0.0007192413,0.00750164],"genre_scores_gemma":[0.9920183,0.00003927717,0.005153411,5.583069e-7,0.00005130435,0.000908672,0.001658618,0.00008252031,0.00008735921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4366632,"threshold_uncertainty_score":0.7230018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02281431786959442,"score_gpt":0.3146150954653549,"score_spread":0.2918007775957605,"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."}}