{"id":"W2937331913","doi":"10.4018/ijsds.2019040105","title":"Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods","year":2019,"lang":"en","type":"article","venue":"International Journal of Strategic Decision Sciences","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Bridge (graph theory); Ground-penetrating radar; Rebar; Artificial neural network; Computer science; Radar; Weibull distribution; Artificial intelligence; Engineering; Structural engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.001487808,0.0001130222,0.0002484468,0.0002955151,0.0001263856,0.000382315,0.00039406,0.00004090864,0.0000424559],"category_scores_gemma":[0.00007244628,0.00008131957,0.0001301564,0.000765612,0.00008291884,0.0003888107,0.0000543356,0.0001653089,0.000002216696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003043217,"about_ca_system_score_gemma":0.00002987515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001791341,"about_ca_topic_score_gemma":0.000004182363,"domain_scores_codex":[0.9984883,0.00007070213,0.0005411118,0.0001751763,0.0005638147,0.0001609089],"domain_scores_gemma":[0.9984409,0.0009461984,0.0002512195,0.00008577705,0.0001971014,0.00007876399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003575536,0.00004500936,0.01003945,0.000009606268,0.0003339653,0.00001268339,0.0001979095,0.4789066,0.3642457,0.015002,0.000009411573,0.131162],"study_design_scores_gemma":[0.0002947976,0.00008977247,0.0719053,0.000190551,0.00007562715,0.00008444716,0.001555829,0.7559282,0.0007712853,0.1687174,0.0001393214,0.0002474765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7754956,0.0002059717,0.2232746,0.00003858907,0.0006470294,0.00004104716,0.000001623179,0.000009920171,0.0002856277],"genre_scores_gemma":[0.6670178,0.00001889682,0.3327484,0.00001301906,0.0001956174,3.334899e-7,5.218681e-7,0.000003627941,0.000001784917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3634744,"threshold_uncertainty_score":0.3686673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1511898172302258,"score_gpt":0.4224009679560264,"score_spread":0.2712111507258007,"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."}}