{"id":"W1987482109","doi":"10.1190/1.2356114","title":"Texture-based classification of ground-penetrating radar images","year":2006,"lang":"en","type":"article","venue":"Geophysics","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ground-penetrating radar; Artificial intelligence; Radar; Computer science; Fourier transform; Pattern recognition (psychology); Radar imaging; Covariance; Classifier (UML); Facies; Geology; Remote sensing; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.00005279493,0.0001058212,0.0001292061,0.00002404406,0.00004638065,0.00001892407,0.0001034093,0.00004089008,0.000006510768],"category_scores_gemma":[0.000006648171,0.0001069914,0.00005723625,0.0002949739,0.00003940898,0.00006542532,0.00000762768,0.00009370086,0.00002991972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001604526,"about_ca_system_score_gemma":0.00001282223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001078021,"about_ca_topic_score_gemma":0.000003810264,"domain_scores_codex":[0.9994139,0.00001579132,0.0001883182,0.0001215256,0.0001170352,0.0001434096],"domain_scores_gemma":[0.9995343,0.00008196773,0.00005172558,0.0002378754,0.00006981822,0.00002429029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000001146279,0.00006121457,0.000128135,0.0000709524,0.000006172936,1.916744e-7,0.0000099075,0.001336845,0.9467405,0.03137488,0.0002213823,0.02004868],"study_design_scores_gemma":[0.0004251618,0.00004459382,0.8200767,0.00004945617,0.00004869695,5.214193e-7,0.00005013331,0.02579933,0.08305509,0.06785639,0.002173077,0.0004208101],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8723509,0.0001075352,0.1175089,0.0001006091,0.0001045164,0.000199899,0.00003947777,0.0002605941,0.009327583],"genre_scores_gemma":[0.9766365,0.000001837073,0.02300248,0.00001323955,0.0001918605,0.00002902349,0.0000539796,0.0000207634,0.00005031675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8636854,"threshold_uncertainty_score":0.4362981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01115869919575544,"score_gpt":0.2320176608597168,"score_spread":0.2208589616639614,"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."}}