{"id":"W2955163944","doi":"10.3390/s19132913","title":"Locating Underground Pipe Using Wideband Chaotic Ground Penetrating Radar","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; Natural Science Foundation of Shanxi Province; National Natural Science Foundation of China","keywords":"Ground-penetrating radar; Chaotic; Radar; Wideband; SIGNAL (programming language); Geology; Remote sensing; Bandwidth (computing); Acoustics; Range (aeronautics); Engineering; Electronic engineering; Computer science; Physics; Telecommunications; Aerospace 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":[0.0001189257,0.000130298,0.0001482302,0.00003874264,0.0001118339,0.00005313967,0.00008544591,0.00005502007,0.00003605495],"category_scores_gemma":[0.00001844177,0.0001392502,0.00004631122,0.000214411,0.0000274829,0.00008049692,0.00001613115,0.0001540704,0.0001225723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005769849,"about_ca_system_score_gemma":0.00001301672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000122064,"about_ca_topic_score_gemma":0.00001051673,"domain_scores_codex":[0.9992378,0.00003006421,0.000170303,0.0001713222,0.0001238964,0.0002666388],"domain_scores_gemma":[0.9994837,0.0001563477,0.0000322173,0.0002316049,0.00002521677,0.00007092125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003369913,0.00004056862,0.0008303832,0.0003284826,0.00006450801,0.000004396321,0.001113957,0.05469197,0.9191527,0.01398802,0.00002248904,0.009759112],"study_design_scores_gemma":[0.002198665,0.0001799533,0.04805387,0.0006740736,0.0002606944,0.00008855397,0.009597442,0.8670071,0.02573998,0.03041294,0.0125588,0.003227934],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865378,0.00006619016,0.005021228,0.00004650265,0.0002132227,0.0001795013,0.000001250408,0.0001709041,0.007763459],"genre_scores_gemma":[0.9766731,0.000007256841,0.0227054,0.00003198323,0.0001670586,0.000003605645,0.000003301074,0.00003989032,0.0003684385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8934128,"threshold_uncertainty_score":0.5678458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02026841765653987,"score_gpt":0.2529081607911081,"score_spread":0.2326397431345683,"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."}}