{"id":"W2106807621","doi":"10.1016/j.jappgeo.2005.10.003","title":"Least-squares local Radon transforms for dip-dependent GPR image decomposition","year":2005,"lang":"en","type":"article","venue":"Journal of Applied Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Ground-penetrating radar; Geology; Radon transform; Interpretability; Radon; Decomposition; Image (mathematics); Remote sensing; Coherence (philosophical gambling strategy); Parametric statistics; Computer science; Pattern recognition (psychology); Artificial intelligence; Mathematics; Radar","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004964671,0.0006361497,0.0006757379,0.0004777179,0.0002579324,0.0006591603,0.000668301,0.0006599843,0.002596288],"category_scores_gemma":[0.002981997,0.0005637715,0.0006411148,0.0006755862,0.0003692116,0.0009344657,0.000818132,0.001368189,0.002645336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001649654,"about_ca_system_score_gemma":0.0008167505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008923775,"about_ca_topic_score_gemma":0.001999133,"domain_scores_codex":[0.9996538,0.0001094143,0.00002293892,0.0000571359,0.0001311098,0.00002568204],"domain_scores_gemma":[0.9991773,0.0003348639,0.00007247453,0.0001747862,0.0002154089,0.00002525308],"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":[0.0003626332,0.0001833437,0.0007591028,0.0003029014,0.00009249086,0.0001353769,0.000193037,0.1105066,0.2142161,0.01427396,0.005748193,0.6532263],"study_design_scores_gemma":[0.00003580166,0.00007242928,0.000910475,0.00001663419,0.00004457764,0.0002196827,0.00006864075,0.9291595,0.05871372,0.006334407,0.004392411,0.0000317366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006023895,0.00006393891,0.9929855,0.00003414787,0.000008624381,0.00001205416,0.00006827181,0.0004893925,0.0003139953],"genre_scores_gemma":[0.09229307,0.0002522328,0.9036366,0.0000387425,0.0000265299,0.0001007768,0.0006783565,0.0005340814,0.00243955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002596288,"threshold_uncertainty_score":0.00868547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006347490282106993,"score_gpt":0.2240787854736348,"score_spread":0.2177312951915278,"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."}}