{"id":"W2795971204","doi":"10.3390/app8040581","title":"Matched-Filter Thermography","year":2018,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Thermography; Optics; Pulse compression; Deconvolution; Acoustics; Filter (signal processing); Materials science; Infrared; Computer science; Computer vision; Physics; Radar; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001890577,0.00009460351,0.00007963966,0.0001148941,0.0001397558,0.00004431824,0.0002539345,0.00004436506,0.0002312066],"category_scores_gemma":[0.000001106503,0.00007520983,0.00003695222,0.0005506447,0.0005237681,0.00005521935,0.00001732791,0.00006014497,0.00002875532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003561084,"about_ca_system_score_gemma":0.000005157271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005008369,"about_ca_topic_score_gemma":0.000004040571,"domain_scores_codex":[0.999408,0.000004486124,0.00008967885,0.0001471677,0.0001322483,0.0002184005],"domain_scores_gemma":[0.9997919,0.00002543065,0.0000122364,0.000119867,0.00001028289,0.00004025448],"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":[0.00001252824,0.00003475812,0.001351905,0.00003290497,0.00005427578,0.000002067891,0.002179937,0.00009865361,0.8734933,0.04110733,0.01293096,0.06870136],"study_design_scores_gemma":[0.0002179265,0.000196628,0.01099522,0.00003035056,0.00002591919,0.000009130667,0.00066663,0.003121471,0.8913444,0.06782407,0.02480005,0.0007682138],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6454769,0.0001217864,0.02023809,0.00002582078,0.0002987442,0.0001592986,0.00001776471,0.001310803,0.3323508],"genre_scores_gemma":[0.9971755,0.00001072438,0.002511231,0.0001307098,0.000120259,0.00002700801,5.478475e-7,0.000009812719,0.00001418122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3516987,"threshold_uncertainty_score":0.3066968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01280782862806219,"score_gpt":0.220625477359355,"score_spread":0.2078176487312928,"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."}}