{"id":"W2011548348","doi":"10.1117/12.383480","title":"<title>Estimation and correction of wavelet dispersion in GPR data</title>","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ground-penetrating radar; Wavelet; Attenuation; Geology; Dispersion (optics); Radar; Inverse; Acoustics; Computer science; Mathematics; Optics; Physics; Geometry; Artificial intelligence; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0008357394,0.0006018207,0.0005537136,0.001548928,0.0003901917,0.001420442,0.0008484271,0.0009304477,0.0426598],"category_scores_gemma":[0.002481619,0.0002539696,0.0002520959,0.001506481,0.000422285,0.00114325,0.0005817519,0.0007880628,0.03611868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004443365,"about_ca_system_score_gemma":0.0004363215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001926098,"about_ca_topic_score_gemma":0.002344964,"domain_scores_codex":[0.9996334,0.00004412538,0.00002051974,0.00005600561,0.000213744,0.00003216923],"domain_scores_gemma":[0.9988382,0.0002273732,0.0000912695,0.0003052748,0.0004896758,0.00004814016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002490214,0.0000840851,0.001084635,0.0002258142,0.00001874241,0.0004502591,0.00007405462,0.003321484,0.166764,0.005694037,0.2363521,0.5856818],"study_design_scores_gemma":[0.00005916378,0.0002837739,0.01091345,0.0001204079,0.0000385126,0.001441172,0.000134795,0.1283545,0.3787597,0.003838943,0.4759533,0.0001021753],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03662942,0.001865567,0.7511841,0.003850723,0.005503146,0.0006117665,0.007971851,0.04872051,0.143663],"genre_scores_gemma":[0.2068473,0.005193609,0.5136643,0.001560391,0.001863177,0.0005560148,0.02084282,0.01433293,0.2351396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0426598,"threshold_uncertainty_score":0.1427112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01173518161430705,"score_gpt":0.2378709752762192,"score_spread":0.2261357936619122,"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."}}