{"id":"W2792650840","doi":"10.1109/access.2018.2801842","title":"A Novel Spatio-Temporal Frequency-Domain Imaging Technique for Two-Layer Materials Using Ultrasonic Arrays","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Universität Duisburg-Essen; University of Ontario Institute of Technology; Shiraz University; Razi University; Sharif University of Technology; Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Computer science; Ultrasonic sensor; Layer (electronics); Frequency domain; Fourier transform; Algorithm; Domain (mathematical analysis); Speed of sound; Acoustics; Scattering; Image (mathematics); Optics; Computer vision; Materials science; Mathematics; Physics","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":[0.0002294258,0.0004619441,0.0003271084,0.0005674474,0.0001890963,0.0004766058,0.0007455805,0.0006738784,0.001143158],"category_scores_gemma":[0.0007224886,0.0003211835,0.0004205229,0.0005827453,0.0002824716,0.001023139,0.000514721,0.0006337895,0.0006270088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002389929,"about_ca_system_score_gemma":0.000349062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004040069,"about_ca_topic_score_gemma":0.0006857354,"domain_scores_codex":[0.9998217,0.00002599896,0.00000992595,0.00003806333,0.00009261081,0.00001172661],"domain_scores_gemma":[0.9996861,0.0001003493,0.00005134951,0.0000559524,0.00008883169,0.00001747322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001127765,0.00008776227,0.0007398769,0.0002018323,0.00004107044,0.0001589688,0.0001582091,0.03041026,0.5084648,0.01034745,0.001481374,0.4477956],"study_design_scores_gemma":[0.00001717421,0.0001367631,0.0006183005,0.00001616032,0.00002811341,0.0007458106,0.00003749011,0.8411595,0.1459039,0.002706134,0.008585002,0.00004573155],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004502645,0.00009801127,0.994534,0.00005026391,0.00002216017,0.00001416053,0.00001826027,0.000259124,0.000501368],"genre_scores_gemma":[0.04547961,0.0001515764,0.9529485,0.00005359787,0.00002671446,0.00004563879,0.00005473689,0.00003336338,0.00120628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001143158,"threshold_uncertainty_score":0.003824234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03019033583317977,"score_gpt":0.2936609351242109,"score_spread":0.2634705992910311,"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."}}