{"id":"W2091310371","doi":"10.2478/v10098-011-0002-z","title":"Periodic structures of Great Lakes levels using wavelet analysis","year":2011,"lang":"en","type":"article","venue":"Journal of Hydrology and Hydromechanics","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Continuous wavelet transform; Wavelet; Environmental science; Scale (ratio); Wavelet transform; Period (music); Hydrology (agriculture); Physical geography; Geology; Climatology; Geography; Discrete wavelet transform; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006227243,0.0001630716,0.0005436506,0.0002158253,0.0001238485,0.000009061388,0.0002625458,0.0001963394,0.0027604],"category_scores_gemma":[0.00008680211,0.000122949,0.0001883436,0.0004097596,0.0003249242,0.0001517266,0.000144863,0.0002874473,0.000004231391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003732376,"about_ca_system_score_gemma":0.0000154931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007509188,"about_ca_topic_score_gemma":0.00004108076,"domain_scores_codex":[0.9986379,0.0001201541,0.0005339305,0.0001994248,0.0002437103,0.0002649545],"domain_scores_gemma":[0.9990467,0.0000524048,0.0005734774,0.0001730982,0.00002442215,0.0001298765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001633035,0.001103195,0.4204261,0.0001258182,0.006170556,0.001734039,0.01652929,0.1527407,0.3727905,0.00476317,0.0002709418,0.02171269],"study_design_scores_gemma":[0.002997901,0.006598099,0.1405623,0.0000777954,0.006662074,0.006784065,0.0001697603,0.6130931,0.03953989,0.1810698,0.001106173,0.001339051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996428,0.0001202214,0.002835673,0.00004845171,0.0001186552,0.00004232961,0.00000674851,0.000008152855,0.0003917708],"genre_scores_gemma":[0.9923623,0.00002287745,0.00741938,0.0001325319,0.00002618438,2.693431e-7,6.233461e-7,0.000009282108,0.00002659286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4603524,"threshold_uncertainty_score":0.9981512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04868294282715133,"score_gpt":0.2498413985697094,"score_spread":0.201158455742558,"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."}}