{"id":"W2019878549","doi":"10.1016/j.jappgeo.2006.10.002","title":"Multi-offset ground penetrating radar data for improved imaging in areas of lateral complexity — Application at a Native American site","year":2006,"lang":"en","type":"article","venue":"Journal of Applied Geophysics","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Offset (computer science); Ground-penetrating radar; Remote sensing; Computer science; Geology; Data quality; Radar; Telecommunications; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002804814,0.0001698153,0.0003904647,0.00007270037,0.00006395596,0.00002832975,0.0003652074,0.0000312438,0.000001020906],"category_scores_gemma":[0.00001042958,0.0001663554,0.000082035,0.0003114586,0.0001036152,0.0001590696,0.00009495232,0.000213847,0.000001855018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009428608,"about_ca_system_score_gemma":0.00002011177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002564709,"about_ca_topic_score_gemma":0.0001269686,"domain_scores_codex":[0.9988232,0.00001810152,0.0005804723,0.0001974068,0.0001626094,0.0002182278],"domain_scores_gemma":[0.9988144,0.000184877,0.0004786149,0.0003445876,0.0001259037,0.00005163526],"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":[0.000102048,0.0002975799,0.001391292,0.0001040318,0.00004008989,6.499039e-7,0.0002058542,0.001412745,0.9664609,0.003147286,0.0001099094,0.02672759],"study_design_scores_gemma":[0.003057863,0.0001052528,0.5532445,0.00007642355,0.0001222801,0.00000787772,0.0002891529,0.3808413,0.01051308,0.0500099,0.001144014,0.0005883001],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9162539,0.00002575898,0.08283011,0.0000593181,0.00003309205,0.0004158843,0.0002366143,0.00002338375,0.0001218954],"genre_scores_gemma":[0.8878327,0.000005090159,0.1116527,0.00002367317,0.0002078389,0.00003101114,0.0002154117,0.00002723162,0.000004376463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9559479,"threshold_uncertainty_score":0.6783775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02457429858400404,"score_gpt":0.2836897379978847,"score_spread":0.2591154394138807,"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."}}