{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001736082,0.0002635618,0.0002616312,0.0001833519,0.0001224019,0.0001794566,0.0003541292,0.0004144417,0.0003674967],"category_scores_gemma":[0.0003584097,0.0001463285,0.0001019427,0.0001602752,0.0003175173,0.0005208168,0.0004750313,0.0001696049,0.0001047102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000128643,"about_ca_system_score_gemma":0.0001563451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002081941,"about_ca_topic_score_gemma":0.0001884528,"domain_scores_codex":[0.9998481,0.00003214682,0.000005961429,0.00003681267,0.00005706238,0.00001986315],"domain_scores_gemma":[0.9997863,0.00003804696,0.00006422234,0.00003766788,0.0000570545,0.00001660872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003162403,0.0000403271,0.00380741,0.000155749,0.00001669246,0.000305828,0.0001347968,0.006837912,0.9519249,0.0007394042,0.0002020024,0.03551873],"study_design_scores_gemma":[0.000115644,0.001885424,0.01408308,0.00003175951,0.00008390003,0.001234168,0.0002064961,0.1663264,0.8126057,0.000754952,0.002610469,0.00006196238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8283703,0.0001954534,0.1692536,0.0001172945,0.00003783711,0.00004623264,0.00005883594,0.0004746455,0.001445836],"genre_scores_gemma":[0.976954,0.00008979724,0.02241331,0.00002319294,0.000007479817,0.00001852002,0.00002902648,0.000006264486,0.0004583854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004144417,"threshold_uncertainty_score":0.001229405,"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."}}