{"id":"W3094111138","doi":"10.1016/j.scitotenv.2020.144612","title":"Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity","year":2020,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; Global Institute for Water Security","funders":"Ministero dell'Ambiente e della Tutela del Territorio e del Mare; Svenska Forskningsrådet Formas","keywords":"Exploit; Cluster analysis; Computer science; Autocorrelation; Scale (ratio); Time series; Entropy (arrow of time); Series (stratigraphy); Feature (linguistics); Data mining; Climatology; Geography; Artificial intelligence; Machine learning; Statistics; Mathematics; Cartography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001960351,0.000387381,0.0004183233,0.001414884,0.0001900817,0.000469262,0.0002533017,0.0002391817,0.0007732942],"category_scores_gemma":[0.0009162107,0.0001336723,0.0004843782,0.001983592,0.0001293753,0.0006007656,0.0004808689,0.000268731,0.0003561704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001579367,"about_ca_system_score_gemma":0.0003097384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002509152,"about_ca_topic_score_gemma":0.003400567,"domain_scores_codex":[0.9998828,0.00001354464,0.00001137089,0.00004135584,0.00002891169,0.0000220106],"domain_scores_gemma":[0.9997548,0.00005884825,0.00005450667,0.000059283,0.0000503438,0.00002223257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004780967,0.0003375838,0.1001428,0.0001932681,0.0003052606,0.0005164444,0.000202487,0.06839248,0.1049448,0.002457656,0.009636419,0.7123927],"study_design_scores_gemma":[0.00002949107,0.0001454973,0.3031592,0.00002194359,0.0001212413,0.0003635434,0.0002145254,0.6701127,0.01374268,0.007160926,0.004881239,0.00004703181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7523495,0.0006233583,0.2374862,0.0003351729,0.0001228885,0.00005742133,0.00557049,0.001615193,0.001839795],"genre_scores_gemma":[0.9662025,0.0001746986,0.02910696,0.00002309292,0.00007645477,0.00002467518,0.003789772,0.00006040772,0.0005413555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002509152,"threshold_uncertainty_score":0.004989147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007337307259773398,"score_gpt":0.1932641657959215,"score_spread":0.1859268585361481,"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."}}