{"id":"W2333217393","doi":"10.1061/40737(2004)261","title":"Drought Frequency Analysis with a Hidden State Markov Model","year":2004,"lang":"en","type":"article","venue":"Critical Transitions in Water and Environmental Resources Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Manitoba Hydro","keywords":"Hidden Markov model; Autocovariance; Markov model; Hidden semi-Markov model; Variable-order Markov model; Markov chain; Markov process; Maximum-entropy Markov model; Computer science; Econometrics; Mathematics; Statistics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001727093,0.000216414,0.0002443335,0.0001770017,0.000231507,0.00004221186,0.0001555006,0.00006199971,0.0007587222],"category_scores_gemma":[0.000001208534,0.0001606309,0.0001029393,0.0003054539,0.0007767752,0.0002432199,0.0001187927,0.0001609737,0.0001150705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001615844,"about_ca_system_score_gemma":0.000001027182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001074754,"about_ca_topic_score_gemma":0.0003518258,"domain_scores_codex":[0.9983612,0.000054038,0.0002625624,0.0005420064,0.0003088909,0.0004712885],"domain_scores_gemma":[0.9995474,0.00001372045,0.00001312573,0.0002687557,9.330831e-7,0.0001561058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003741079,0.002401134,0.1026269,0.0001192063,0.001618228,0.001528688,0.02027416,0.8572831,0.002748203,0.001992504,0.00003861583,0.008995214],"study_design_scores_gemma":[0.01281148,0.001434637,0.4184566,0.0002176132,0.01319849,0.0001641843,0.01075089,0.2407146,0.003735301,0.2907837,0.002725023,0.00500745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500518,0.00004598463,0.03833803,0.001754639,0.000007600322,0.0001757923,0.00002652554,0.00004146882,0.009558155],"genre_scores_gemma":[0.9894137,0.00007893378,0.009528905,0.000472884,0.000006172931,0.00006280677,0.00006658318,0.00001884627,0.0003511249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6165685,"threshold_uncertainty_score":0.8307476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005166868895561801,"score_gpt":0.2049092146234261,"score_spread":0.1997423457278643,"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."}}