{"id":"W3154660791","doi":"10.5194/nhess-2021-110","title":"Applying machine learning for drought prediction using data from a large ensemble of climate simulations","year":2021,"lang":"en","type":"article","venue":"","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Leibniz-Rechenzentrum; Leibniz-Gemeinschaft; Environment and Climate Change Canada; Bayerische Akademie der Wissenschaften","keywords":"Context (archaeology); Climatology; Precipitation; Index (typography); Machine learning; Computer science; Artificial intelligence; Climate model; Environmental science; Meteorology; Climate change; Geography; Geology","routes":{"ca_aff":false,"ca_fund":true,"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":[0.001502683,0.0005291828,0.0004613023,0.0008746839,0.0003115729,0.0004812085,0.0003627183,0.0005401295,0.000448427],"category_scores_gemma":[0.003281467,0.0002056643,0.0005259908,0.0006895015,0.0001696505,0.0005038511,0.0003703058,0.0005807832,0.0001015317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007469353,"about_ca_system_score_gemma":0.0003907227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01414533,"about_ca_topic_score_gemma":0.01155785,"domain_scores_codex":[0.9996502,0.0001765299,0.00002333556,0.00007163368,0.00004640579,0.00003192838],"domain_scores_gemma":[0.9985725,0.0008480122,0.0001001342,0.0002028662,0.0002076303,0.00006887833],"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":[0.0001851195,0.0001211852,0.0439025,0.00002616015,0.0002365047,0.00006115618,0.00003475308,0.9324956,0.0008122801,0.000197562,0.0004150854,0.02151209],"study_design_scores_gemma":[0.000006519746,0.00002750146,0.008319259,0.000003479576,0.00001488442,0.000005707753,0.00001078587,0.990822,0.0004337301,0.0002151083,0.0001360654,0.000005083742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862034,0.0001784268,0.0121096,0.0001154269,0.00004582528,0.00002051747,0.0005199858,0.0001557588,0.000651004],"genre_scores_gemma":[0.9959999,0.00003220928,0.003281686,0.00001004625,0.00001050274,0.00001065297,0.0005487604,0.000005902575,0.0001004898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01414533,"threshold_uncertainty_score":0.02812594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03725620047578235,"score_gpt":0.2918349943100176,"score_spread":0.2545787938342352,"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."}}