{"id":"W2966582723","doi":"10.18280/isi.240213","title":"Ground Penetrating Radar Weak Signals Denoising via Semi-soft Threshold Empirical Wavelet Transform","year":2019,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Railway","keywords":"Ground-penetrating radar; Wavelet transform; Noise reduction; Wavelet; Acoustics; Radar; Computer science; Artificial intelligence; Physics; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000328699,0.0002483263,0.0002859584,0.0001260362,0.0002229837,0.0002405299,0.0001982973,0.0001529849,0.0001006219],"category_scores_gemma":[0.00002769581,0.0002426594,0.0001133157,0.0004047941,0.0000509344,0.001887518,0.00002506559,0.0002710525,0.0004310628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001870185,"about_ca_system_score_gemma":0.0000244541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002935871,"about_ca_topic_score_gemma":0.000005066378,"domain_scores_codex":[0.9985062,0.0000256312,0.0006391936,0.0001439453,0.0002855333,0.0003995195],"domain_scores_gemma":[0.9992781,0.0001382847,0.0001041513,0.0002695345,0.00009645672,0.000113524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000514527,0.0001043033,0.002440681,0.003072432,0.0002509186,0.000002741836,0.01410868,0.02197093,0.2405725,0.009151902,0.0009340168,0.7073395],"study_design_scores_gemma":[0.003151608,0.0006251513,0.1015131,0.001454333,0.000222681,0.000182793,0.006217092,0.6821097,0.05611912,0.08994613,0.05467739,0.003780893],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7337109,0.00008844482,0.2282589,0.00004959749,0.0002145645,0.0005640576,0.00001722387,0.000433839,0.03666249],"genre_scores_gemma":[0.990799,0.00001337137,0.008724503,0.0001067929,0.0001133697,0.00006682285,0.00009118912,0.00003070477,0.00005421723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7035586,"threshold_uncertainty_score":0.9895363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0167860904042901,"score_gpt":0.2521104533027246,"score_spread":0.2353243628984345,"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."}}