{"id":"W4400367158","doi":"10.1002/env.2864","title":"Assessing predictability of environmental time series with statistical and machine learning models","year":2024,"lang":"en","type":"article","venue":"Environmetrics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute of Environmental Health Sciences; Princess Nourah Bint Abdulrahman University; National Science Foundation","keywords":"Computer science; Machine learning; Predictability; Artificial intelligence; Popularity; Baseline (sea); Statistical model; Artificial neural network; Class (philosophy); Data science; Statistics; Mathematics; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004794244,0.0008402478,0.0005784991,0.001992093,0.0003471923,0.002605161,0.0008826185,0.001004084,0.001174967],"category_scores_gemma":[0.01957944,0.0002375025,0.0007928159,0.002121897,0.001214376,0.003668321,0.001366731,0.001491398,0.0002145698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001094346,"about_ca_system_score_gemma":0.001190599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005007535,"about_ca_topic_score_gemma":0.003750923,"domain_scores_codex":[0.9982151,0.001004394,0.0001111673,0.000177191,0.0004162062,0.00007597387],"domain_scores_gemma":[0.9849834,0.01222642,0.001263777,0.0008919351,0.0004538203,0.0001806518],"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":[0.00005433073,0.0000506435,0.01231658,0.00009434475,0.00009359109,0.0001364868,0.0001523306,0.876794,0.0005029808,0.07879569,0.000688574,0.03032049],"study_design_scores_gemma":[0.000002673994,0.00002568944,0.001798537,0.00002084529,0.000009033902,0.00002980647,0.00006496908,0.9464909,0.0002109814,0.05078026,0.0005540231,0.00001226005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2133993,0.001736753,0.7677481,0.0059374,0.0001358752,0.00007267034,0.0004934523,0.0005524207,0.009923929],"genre_scores_gemma":[0.9265834,0.0009373191,0.07125003,0.0001392338,0.0001682966,0.00005877938,0.0002879316,0.00004834511,0.0005266904],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005007535,"threshold_uncertainty_score":0.02535468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01687572721681556,"score_gpt":0.2028063077366,"score_spread":0.1859305805197844,"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."}}