{"id":"W4225375074","doi":"10.37394/232015.2022.18.46","title":"Forecasting the Long-term Monthly Variations of Major Floods","year":2022,"lang":"en","type":"article","venue":"WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Term (time); Markov chain; Environmental science; Climatology; Duration (music); Climate change; Meteorology; Econometrics; Geography; Statistics; Mathematics; Geology","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":[],"category_scores_codex":[0.0002799062,0.0001187958,0.0001164779,0.00004743567,0.001033233,0.000008471323,0.0001493256,0.00003012453,0.009755656],"category_scores_gemma":[0.000001796432,0.00009603614,0.00005391457,0.0001344776,0.000143527,0.00006694337,0.0000416421,0.0001650205,0.00005866857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001440817,"about_ca_system_score_gemma":0.00001336757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003380346,"about_ca_topic_score_gemma":0.00005554256,"domain_scores_codex":[0.9989313,0.00008219114,0.000233141,0.0002438457,0.0003286052,0.0001808873],"domain_scores_gemma":[0.9996164,0.00005940434,0.00007531218,0.0001878382,0.000001217023,0.00005984617],"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":[0.0002013131,0.00164518,0.2269147,0.00002027408,0.0005857872,0.00002674716,0.007898014,0.6032844,0.0009198044,0.000129078,0.0002344271,0.1581403],"study_design_scores_gemma":[0.001738855,0.0003882268,0.9367129,0.00001133292,0.0004106736,0.00003751758,0.0006570753,0.02674645,0.004506578,0.0005373318,0.02752287,0.0007302575],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8897787,0.0001114174,0.1057401,0.00081676,0.0001082343,0.0003596573,0.00002627691,0.00002564143,0.003033112],"genre_scores_gemma":[0.9965939,0.00002045597,0.001952879,0.0001544521,0.000005887643,0.000158702,0.0000152684,0.000008770277,0.00108969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7097981,"threshold_uncertainty_score":0.9911495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01359937398284632,"score_gpt":0.1975860691744542,"score_spread":0.1839866951916079,"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."}}