{"id":"W622538642","doi":"","title":"Assessing the Benefits of Ground Penetrating Radar Technology - Does It Improve the Accuracy of FWD Results and Overlay Design?","year":2012,"lang":"en","type":"article","venue":"2012 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: INNOVATIONS AND OPPORTUNITIES","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ground-penetrating radar; Falling weight deflectometer; Overlay; Pavement management; Engineering; Radar; Asset management; Pavement engineering; Civil engineering; Computer science; Asphalt; Geography; Subgrade; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009058521,0.000761718,0.0006400903,0.001320589,0.0002927785,0.002532702,0.000949408,0.001592686,0.002300852],"category_scores_gemma":[0.02470198,0.0003268976,0.0004125143,0.001150839,0.0007852161,0.004169466,0.0008522081,0.0004385902,0.0007811066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001389176,"about_ca_system_score_gemma":0.001189371,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0155656,"about_ca_topic_score_gemma":0.04597766,"domain_scores_codex":[0.9951426,0.001656438,0.00023301,0.0003251853,0.00236649,0.0002763581],"domain_scores_gemma":[0.9895985,0.004311063,0.001385451,0.0008125261,0.003658846,0.0002335895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0012733,0.0004341472,0.4885391,0.0008926452,0.0003674201,0.0004930574,0.0007014472,0.03495121,0.03806344,0.002647582,0.001823656,0.429813],"study_design_scores_gemma":[0.000158053,0.003896626,0.7788212,0.0004194654,0.0009942499,0.001437109,0.005652669,0.1226873,0.0669494,0.004721388,0.01401483,0.000247727],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9364761,0.004121029,0.03856581,0.002683417,0.0001284478,0.0001687902,0.000590862,0.0003326694,0.01693278],"genre_scores_gemma":[0.9679434,0.001163119,0.02969675,0.0001440779,0.00003665569,0.00001266355,0.0001767591,0.00003821262,0.0007883655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9844344,"threshold_uncertainty_score":0.04790658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04303992150414112,"score_gpt":0.2611293191715509,"score_spread":0.2180893976674097,"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."}}