{"id":"W1578538178","doi":"10.1029/2004wr003605","title":"A Markov switching model for annual hydrologic time series","year":2005,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autocorrelation; Markov chain; Autoregressive–moving-average model; Series (stratigraphy); Stochastic modelling; Hidden Markov model; Econometrics; Mathematics; Markov model; Statistics; Markov property; Time series; Applied mathematics; Sequence (biology); Computer science; Autoregressive model; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001911739,0.0001331171,0.0001820609,0.0001199145,0.000604348,0.00005872634,0.0005005572,0.000141869,0.003148568],"category_scores_gemma":[0.00005453894,0.00008833326,0.0000998852,0.0001887071,0.0003231468,0.0003564403,0.000525177,0.0003128976,0.003696018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009158765,"about_ca_system_score_gemma":0.000004888491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001140691,"about_ca_topic_score_gemma":0.0001892957,"domain_scores_codex":[0.997807,0.0002102965,0.0002005564,0.00044498,0.0004850489,0.0008521201],"domain_scores_gemma":[0.9994138,0.00008967984,0.00002040759,0.0003212277,0.00002011021,0.0001347558],"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":[0.002273103,0.0006074952,0.03336015,0.00005437254,0.0002477231,0.00006961981,0.07893157,0.6420483,0.1494998,0.00009406453,0.05684158,0.03597223],"study_design_scores_gemma":[0.000276597,0.0001455978,0.0002082253,0.000002696538,0.0000126728,0.000009185826,0.00009066911,0.8308466,0.003845776,0.002889167,0.1615056,0.0001672384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798542,0.00003604202,0.0009520327,0.004749148,0.000007132205,0.0002432369,0.00001194048,0.00005469574,0.01409158],"genre_scores_gemma":[0.9379336,0.000007034254,0.002934328,0.0002573592,0.0001177712,0.00008397677,0.00001403016,0.00001800524,0.05863394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1887983,"threshold_uncertainty_score":0.9977627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02266867524783588,"score_gpt":0.2924706430107785,"score_spread":0.2698019677629426,"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."}}