{"id":"W4225911863","doi":"10.4018/978-1-6684-2408-7.ch049","title":"Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods","year":2021,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Bridge (graph theory); Artificial neural network; Ground-penetrating radar; Rebar; Engineering; Weibull distribution; Radar; Computer science; Structural engineering; Artificial intelligence; Statistics; Mathematics","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":[0.0006090063,0.001058946,0.000460451,0.001451133,0.0001882887,0.00106299,0.0007217306,0.0008233876,0.001990802],"category_scores_gemma":[0.001278023,0.0003639626,0.0006500556,0.001832365,0.0001758451,0.0009405491,0.0004025921,0.000575235,0.0007448963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003323764,"about_ca_system_score_gemma":0.0003079385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004312312,"about_ca_topic_score_gemma":0.004134952,"domain_scores_codex":[0.9997025,0.00007192377,0.00002116054,0.00009881826,0.00008305562,0.00002248358],"domain_scores_gemma":[0.9996551,0.0001895934,0.00004917399,0.00001965065,0.00007853853,0.000007825401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000862817,0.0001338472,0.003020312,0.0001904857,0.000103662,0.0001796276,0.00007026074,0.3937048,0.01131047,0.00188603,0.001864349,0.5874498],"study_design_scores_gemma":[0.000001799502,0.00001818083,0.001003438,0.000008330572,0.0000096412,0.00002119672,0.00001520971,0.9967231,0.001163939,0.0006052174,0.0004223247,0.000007615682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.039529,0.0008198969,0.9539127,0.0001319976,0.00005171772,0.00006426658,0.0002473583,0.001129168,0.004113934],"genre_scores_gemma":[0.4334755,0.001424569,0.5552558,0.00009023749,0.00006938531,0.0001943067,0.0007177197,0.0001352129,0.008637325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004312312,"threshold_uncertainty_score":0.008574426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05854133420500565,"score_gpt":0.3242383386289114,"score_spread":0.2656970044239058,"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."}}