{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007648483,0.001056166,0.0005318735,0.00146588,0.0002284953,0.0008237208,0.0007338155,0.0008269291,0.0009955743],"category_scores_gemma":[0.001692791,0.0003724887,0.0006600642,0.001357408,0.0001996907,0.0007820738,0.0004512343,0.0006363583,0.0003104968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003828669,"about_ca_system_score_gemma":0.0004244322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005514413,"about_ca_topic_score_gemma":0.00410879,"domain_scores_codex":[0.9995568,0.0001171099,0.0000349133,0.0001364086,0.0001091018,0.00004555264],"domain_scores_gemma":[0.9994584,0.0002684842,0.0001008047,0.00002501428,0.0001317482,0.0000155704],"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":[0.0001311627,0.0002499655,0.006058619,0.0001171782,0.0001288383,0.0001862274,0.00006777192,0.7094747,0.008913516,0.001201205,0.0007472725,0.2727235],"study_design_scores_gemma":[0.00000183225,0.00001580022,0.0006947007,0.000002821499,0.000005787077,0.000008759702,0.000008180193,0.9983972,0.0005848131,0.0002015531,0.00007405932,0.000004393198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1162113,0.0003945038,0.8803079,0.0001370582,0.00004029305,0.000082382,0.0001621844,0.000723856,0.00194061],"genre_scores_gemma":[0.7681645,0.0004233419,0.2285704,0.00005327027,0.00003736714,0.0001571066,0.0002886932,0.00004170642,0.002263617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005514413,"threshold_uncertainty_score":0.01096463,"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."}}