{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002554883,0.0004515492,0.0007458958,0.00007601119,0.0002676612,0.0002140579,0.0001527164,0.0003042493,0.00001342573],"category_scores_gemma":[0.00002649521,0.0004517174,0.0003338814,0.0001448361,0.00007313568,0.00004395346,0.0001484722,0.0003893625,0.000002381113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001120351,"about_ca_system_score_gemma":0.00002626348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009117086,"about_ca_topic_score_gemma":0.00006827745,"domain_scores_codex":[0.9982805,0.00008312453,0.0005113696,0.0005304171,0.0002095444,0.0003851005],"domain_scores_gemma":[0.9989403,0.0002518775,0.0001591059,0.0004211447,0.00006747161,0.0001601398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000009938823,0.00001090783,0.0001000799,0.0001914336,0.001278304,0.00003227942,0.00009031218,0.01284232,0.03393162,0.8346627,0.00002919107,0.1168209],"study_design_scores_gemma":[0.0004019954,0.00005145575,0.003967721,0.001083649,0.00282803,0.00005817073,0.0002024015,0.1976053,0.0005086148,0.7844846,0.006273066,0.002534984],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04511654,0.004743286,0.6280839,0.00002963404,0.0009388689,0.0008036201,0.0001622787,0.0006450559,0.3194767],"genre_scores_gemma":[0.3527861,0.0000128589,0.6448609,0.00006076356,0.001127878,0.00001485599,0.00003327128,0.0000908564,0.001012468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3184643,"threshold_uncertainty_score":0.9997935,"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."}}