{"id":"W4362559509","doi":"10.3390/su15076160","title":"A Contemporary Review on Deep Learning Models for Drought Prediction","year":2023,"lang":"en","type":"review","venue":"Sustainability","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Machine learning; Field (mathematics); Artificial neural network; Convolutional neural network; Perceptron; Predictive modelling; Data science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002326999,0.000425138,0.001630235,0.00008790773,0.0003156779,0.00001832466,0.0003789756,0.0004197561,0.0003067339],"category_scores_gemma":[0.00164659,0.000339442,0.000989415,0.0008229116,0.0002302988,0.0002196151,0.0002311139,0.0006169469,0.000336971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001567428,"about_ca_system_score_gemma":0.0002150968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001032178,"about_ca_topic_score_gemma":0.00002195655,"domain_scores_codex":[0.9966857,0.0007463334,0.0008307937,0.0009697267,0.0003047382,0.0004627272],"domain_scores_gemma":[0.9981566,0.000577183,0.0003891716,0.0006891912,0.00006258234,0.000125271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002777501,0.0001455554,0.0001515987,0.111646,0.0002256395,0.00002518631,0.0001045673,0.00341919,4.778175e-9,0.000308389,0.01416794,0.8697781],"study_design_scores_gemma":[0.00009877958,0.0001393268,0.000007496974,0.003722637,0.0007381071,0.000002887145,0.00002167794,0.003545087,2.274178e-8,0.01137332,0.9800733,0.0002773511],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000002793986,0.9899266,0.001676392,0.0002723843,0.00009377721,0.003253841,0.00003612196,0.0002174739,0.004520623],"genre_scores_gemma":[0.0001357414,0.9929238,0.00004193799,0.0001523567,0.00008070869,0.001531921,0.0003621157,0.00005490533,0.004716524],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9659054,"threshold_uncertainty_score":0.9999058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05786910709894943,"score_gpt":0.3324434142700302,"score_spread":0.2745743071710808,"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."}}