{"id":"W3122319633","doi":"","title":"An intuitive guide to wavelets for economists","year":2005,"lang":"en","type":"article","venue":"Econstor (Econstor)","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wavelet; Exploratory analysis; Computer science; Legendre wavelet; Economic analysis; Data science; Management science; Engineering economics; Wavelet transform; Industrial engineering; Economics; Operations research; Discrete wavelet transform; Artificial intelligence; Mathematics; Engineering; Finance; Classical economics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00126002,0.0003353072,0.0004610296,0.00031027,0.0003036126,0.0003467114,0.001313313,0.0001446091,0.0002463616],"category_scores_gemma":[0.0002366001,0.0003611774,0.0002063556,0.0001995215,0.0001248244,0.001337879,0.0001767999,0.0001827881,0.0007702416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003774896,"about_ca_system_score_gemma":0.0003964122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003194646,"about_ca_topic_score_gemma":0.00009699495,"domain_scores_codex":[0.9974313,0.000171348,0.000618161,0.0009346736,0.0001553314,0.0006891526],"domain_scores_gemma":[0.9976515,0.0004598136,0.0001795416,0.001022088,0.0002093263,0.0004777042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001974747,0.0005785577,0.02116682,0.0000633487,0.0002225952,0.00009117807,0.004928115,0.0008880348,0.01829563,0.09144145,0.1746085,0.6875183],"study_design_scores_gemma":[0.0041902,0.00110698,0.01898128,0.000101844,0.00004993473,0.0003730526,0.0001431717,0.04042811,0.07752852,0.005098046,0.8498776,0.00212124],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2721141,0.0001459928,0.7033416,0.001632418,0.001408586,0.0005497137,0.00003468063,0.0002756813,0.02049725],"genre_scores_gemma":[0.3345813,0.000007214736,0.6542442,0.005717227,0.001111988,0.0001333179,0.000009200565,0.00004460593,0.004150909],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6853971,"threshold_uncertainty_score":0.999884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01770791875906682,"score_gpt":0.2982882545485855,"score_spread":0.2805803357895187,"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."}}