{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002244815,0.0001282374,0.0001754482,0.00001080465,0.000386607,0.0001260616,0.0007538933,0.00004028479,0.0001349931],"category_scores_gemma":[0.00006329035,0.0000734362,0.00005512453,0.0002042484,0.0006047948,0.0004223183,0.0008633372,0.0001264802,0.0000188877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000540928,"about_ca_system_score_gemma":0.00002301529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001043388,"about_ca_topic_score_gemma":0.00000266761,"domain_scores_codex":[0.9985995,0.0000864698,0.0002048539,0.0003833575,0.0005106725,0.0002151194],"domain_scores_gemma":[0.9992369,0.00005552624,0.000190368,0.0003929565,0.00001039675,0.0001138717],"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":[0.0004020708,0.0009249509,0.01456528,0.0001166321,0.0003490578,0.00005312614,0.01330215,0.5221792,0.3755202,0.01034268,0.0005469696,0.06169772],"study_design_scores_gemma":[0.00009119725,0.000114489,0.2287573,0.000009391334,0.00003570633,0.000006549517,0.00007360657,0.7681133,0.001635899,0.001027586,0.00003094814,0.0001040207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9104041,0.00002407325,0.07403163,0.01483179,0.00007951647,0.0002119585,0.0002057307,0.00002584306,0.0001853421],"genre_scores_gemma":[0.994501,0.00001241219,0.005273927,0.0001148094,0.00004228123,0.000004561735,0.000007557176,0.0000031794,0.00004026149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3738843,"threshold_uncertainty_score":0.2994641,"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."}}