{"id":"W2113502054","doi":"10.1080/02626667.2012.672741","title":"Prediction of hydrological drought durations based on Markov chains: case of the Canadian prairies","year":2012,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain; Statistics; Mathematics; Autocorrelation; Autoregressive model; Environmental science; Streamflow; Hydrology (agriculture); Drainage basin; Geography; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001809324,0.0005762752,0.0006250208,0.0008630928,0.000845616,0.0008374649,0.00108749,0.0008309614,0.0006692488],"category_scores_gemma":[0.006055326,0.0004713211,0.0005895588,0.0007222535,0.0008260643,0.0005670535,0.0004939867,0.0008092615,0.00005907437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004723782,"about_ca_system_score_gemma":0.004787868,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8098847,"about_ca_topic_score_gemma":0.6593398,"domain_scores_codex":[0.9996305,0.00008551889,0.0000190801,0.00008328068,0.00005582404,0.0001258673],"domain_scores_gemma":[0.996926,0.001995464,0.0002822594,0.0001082291,0.0004906199,0.0001974631],"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":[0.00006887659,0.00002232369,0.01529211,0.00001633602,0.00002936969,0.00009344827,0.00007212321,0.9791869,0.0002921545,0.001596432,0.000139749,0.003190212],"study_design_scores_gemma":[0.000006827782,0.000007394219,0.00301142,0.000002982626,0.000006033702,0.00000570531,0.0000168322,0.9964716,0.00006045011,0.0003427699,0.00006082391,0.000007133685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9763436,0.0001452509,0.02148344,0.0002624934,0.000009167783,0.0000657023,0.0005084724,0.00009550325,0.00108635],"genre_scores_gemma":[0.9954358,0.0001065997,0.003587407,0.00001453249,0.000005533809,0.00002404644,0.0002982421,0.000005679191,0.0005221477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1901153,"threshold_uncertainty_score":0.3824698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0275087854726319,"score_gpt":0.2468960038946349,"score_spread":0.219387218422003,"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."}}