{"id":"W1970456366","doi":"10.4028/www.scientific.net/kem.413-414.305","title":"Crack Size Estimation Using a Combination of Cross Correlation and Phase Shift Correction in Ultrasonic Time-of-Flight Diffraction Method","year":2009,"lang":"en","type":"article","venue":"Key engineering materials","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province","keywords":"Sizing; Diffraction; Cross-correlation; Time of flight; Ultrasonic sensor; SIGNAL (programming language); Acoustics; Phase (matter); Correlation function (quantum field theory); Materials science; Function (biology); Transmission (telecommunications); Optics; Computer science; Physics; Mathematics; Statistics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00100099,0.0006506813,0.0007177917,0.001870081,0.0002888187,0.0005367581,0.0007525926,0.0008200199,0.0008230276],"category_scores_gemma":[0.002599118,0.000497933,0.0004158264,0.001375259,0.0003195769,0.00107595,0.0005781675,0.000596941,0.0003217861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003327736,"about_ca_system_score_gemma":0.00068761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001651293,"about_ca_topic_score_gemma":0.00228219,"domain_scores_codex":[0.9990284,0.000181667,0.00004022608,0.0001679506,0.0005284274,0.00005328811],"domain_scores_gemma":[0.9981944,0.0006579894,0.0001701328,0.0001223443,0.0008084591,0.00004671723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000391606,0.0002021301,0.007175758,0.0002802954,0.0001530366,0.0002850735,0.0001301172,0.03513515,0.3931937,0.003052174,0.001268259,0.5587327],"study_design_scores_gemma":[0.00002958056,0.0002253209,0.008013383,0.00001723409,0.00006588596,0.0009090563,0.00002864171,0.826933,0.1608908,0.0005881494,0.002195013,0.0001040089],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05203614,0.0004670516,0.9458885,0.00006961558,0.00005193391,0.00004322942,0.00004937995,0.0006136126,0.0007805417],"genre_scores_gemma":[0.2758325,0.000392615,0.7223476,0.00004511463,0.00003822123,0.00005551269,0.0001110037,0.00006726389,0.001110224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001870081,"threshold_uncertainty_score":0.005293787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007247749004763406,"score_gpt":0.2638528555146764,"score_spread":0.256605106509913,"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."}}