{"id":"W2067941349","doi":"10.1617/s11527-013-0093-6","title":"In situ assessment of structural timber using non-destructive techniques","year":2013,"lang":"en","type":"article","venue":"Materials and Structures","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":131,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nondestructive testing; Ground-penetrating radar; Identification (biology); Forensic engineering; Engineering; Visual inspection; Construction engineering; Solid mechanics; Concrete cover; Civil engineering; Computer science; Radar; Structural engineering; Reinforced concrete; Artificial intelligence; Materials science","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":[0.0002524061,0.0004391835,0.0002842921,0.0006688308,0.0002767781,0.0004689459,0.0004794371,0.0004766351,0.0009746342],"category_scores_gemma":[0.0003165024,0.0003039301,0.0001270101,0.0003899833,0.0003628254,0.0007610509,0.0003759775,0.0003108173,0.0003449755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001418638,"about_ca_system_score_gemma":0.0001901073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000732525,"about_ca_topic_score_gemma":0.004891986,"domain_scores_codex":[0.9997681,0.00002707054,0.000008265471,0.00004243866,0.0001341607,0.00002004464],"domain_scores_gemma":[0.9997024,0.0001256829,0.00003726107,0.00002689723,0.00009429546,0.00001347409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008390519,0.00007500182,0.00650872,0.0001399129,0.00001590344,0.0001095904,0.0001981602,0.002907154,0.9636803,0.0002631882,0.0001280161,0.02589023],"study_design_scores_gemma":[0.00002243879,0.0003753314,0.09076545,0.00004636244,0.0001350551,0.00158416,0.001100959,0.08633632,0.814149,0.00151554,0.003906623,0.00006270756],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8294787,0.0006854208,0.1626787,0.0000439586,0.00002979336,0.00006145543,0.0003408185,0.0002123179,0.006468873],"genre_scores_gemma":[0.9334307,0.0005418997,0.06263718,0.0000362188,0.00002454635,0.0000459191,0.0002153247,0.00003827694,0.003030027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009746342,"threshold_uncertainty_score":0.003260434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009355665801729283,"score_gpt":0.2969831037626927,"score_spread":0.2876274379609634,"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."}}