{"id":"W2795367766","doi":"10.1063/1.5016339","title":"Single frequency thermal wave radar: A next-generation dynamic thermography for quantitative non-destructive imaging over wide modulation frequency ranges","year":2018,"lang":"en","type":"article","venue":"Review of Scientific Instruments","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Thermography; Undersampling; Materials science; Frame rate; Frequency modulation; Waveform; Optics; Modulation (music); Image quality; Acoustics; Center frequency; Frequency domain; Radar; Infrared; Computer science; Radio frequency; Physics; Telecommunications; Band-pass filter; Artificial intelligence; Computer vision; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005699105,0.0002656257,0.0003478818,0.0003171668,0.0002373067,0.00009547434,0.0002216518,0.0000692011,0.00008117749],"category_scores_gemma":[0.00004473871,0.0002436227,0.0002374274,0.0007020781,0.0003952252,0.0006560905,0.00002247879,0.0001009446,0.000002281545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009658372,"about_ca_system_score_gemma":0.0000343573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002418836,"about_ca_topic_score_gemma":0.00001190855,"domain_scores_codex":[0.9983749,0.00006255027,0.0005365607,0.0003885581,0.0003175179,0.000319943],"domain_scores_gemma":[0.9989679,0.00005037089,0.0002353831,0.0003902487,0.0002956303,0.00006045473],"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":[0.000007581478,0.00003847057,0.0003567268,0.001108384,0.00007666655,4.715325e-7,0.0004750428,0.000002224025,0.8818445,0.0004378131,0.0001736047,0.1154786],"study_design_scores_gemma":[0.002463885,0.0009805084,0.02608563,0.02045326,0.0007547975,0.00002421046,0.0004958385,0.2225749,0.6880366,0.03468296,0.001126762,0.00232069],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811399,0.004350947,0.0103743,0.00002217399,0.001214259,0.001384826,0.0005006121,0.0001585354,0.0008544535],"genre_scores_gemma":[0.9775078,0.0008677937,0.02113754,0.00005190528,0.00005845197,0.0001142669,0.0002111251,0.00004249848,0.00000862963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2225727,"threshold_uncertainty_score":0.9934645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02820729558460324,"score_gpt":0.2634255571187401,"score_spread":0.2352182615341368,"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."}}