{"id":"W2381192871","doi":"","title":"Target echo signal feature extraction and material recognition of ultra-wideband ground-penetrating radar","year":2009,"lang":"en","type":"article","venue":"","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Computer science; Ground-penetrating radar; Echo (communications protocol); Radar; SIGNAL (programming language); Feature extraction; Wideband; Acoustics; Artificial intelligence; Wavelet; Broadband; Feature (linguistics); Remote sensing; Pattern recognition (psychology); Geology; Telecommunications; Electronic engineering; Physics; 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":[0.00006573838,0.00007672838,0.00009736337,0.00002223618,0.00003735143,0.00002757328,0.00002627438,0.0000585523,0.00008761475],"category_scores_gemma":[0.000006038707,0.00007016316,0.00002201443,0.00007081694,0.00001030681,0.0001232498,0.000001631925,0.00008086881,0.000003552861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007654265,"about_ca_system_score_gemma":0.000002452895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001329206,"about_ca_topic_score_gemma":0.000001332048,"domain_scores_codex":[0.9996271,0.00001435758,0.0001138225,0.00009525748,0.0000588233,0.00009060587],"domain_scores_gemma":[0.9998198,0.00004283843,0.00002449058,0.0000579916,0.00002018161,0.00003465513],"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":[0.000005338742,0.00001591403,0.00001042872,0.00001835279,0.000004491692,2.305203e-7,0.00002287512,0.00002777336,0.9594287,0.0002355237,0.000116497,0.04011392],"study_design_scores_gemma":[0.0002646645,0.0001066477,0.02078552,0.00003553589,0.0000217378,0.00001131562,0.00008662579,0.0007194255,0.950074,0.02722893,0.0004637179,0.0002019118],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9316735,0.00004254754,0.05892827,0.0001130369,0.00008967314,0.000175251,0.00002877515,0.0001493257,0.008799684],"genre_scores_gemma":[0.9119076,0.00001795774,0.08783203,0.00002462373,0.0001308011,0.000005417222,0.0000340603,0.000006716584,0.00004075766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03991201,"threshold_uncertainty_score":0.286117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01204371438659145,"score_gpt":0.2450513621123606,"score_spread":0.2330076477257692,"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."}}