{"id":"W2745465687","doi":"10.1002/lpor.201600342","title":"Terahertz Thermometry: Combining Hyperspectral Imaging and Temperature Mapping at Terahertz Frequencies","year":2017,"lang":"en","type":"article","venue":"Laser & Photonics Review","topic":"Terahertz technology and applications","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Concordia University; Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Concordia University; Volkswagen Foundation","keywords":"Terahertz radiation; Hyperspectral imaging; Photothermal therapy; Chemical imaging; Optics; Materials science; Measure (data warehouse); Image resolution; Biological system; Physics; Computer science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000193249,0.0002890867,0.0004127495,0.00006859869,0.0006246321,0.0001646987,0.0005086492,0.0001246529,0.0001207774],"category_scores_gemma":[0.00004927452,0.0002614936,0.00009828142,0.000137579,0.000163998,0.0002534063,0.0001277068,0.0004391449,0.00006261402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009672486,"about_ca_system_score_gemma":0.00001422507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001792464,"about_ca_topic_score_gemma":0.00002251689,"domain_scores_codex":[0.9988686,0.00002174487,0.0002864125,0.0003394417,0.0001154407,0.0003683885],"domain_scores_gemma":[0.998761,0.00004227881,0.0001009361,0.0009706282,0.00003521158,0.00008996414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001062077,0.0001218718,0.02100121,0.005558946,0.0005022396,0.0001898499,0.00126202,0.00001575602,0.4461918,0.005275686,0.02361274,0.4962572],"study_design_scores_gemma":[0.0005661646,0.00001876444,0.009204808,0.003385299,0.0001214111,0.0002476738,0.0001005449,0.002272152,0.02464181,0.001111082,0.9575139,0.0008164416],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7260214,0.2572553,0.00003328149,0.004372203,0.0002778125,0.0007234151,0.00003769244,0.0008162008,0.01046266],"genre_scores_gemma":[0.9218953,0.07422487,0.002016797,0.001141367,0.00003753134,0.000177762,0.00002135043,0.00006715947,0.0004178126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9339011,"threshold_uncertainty_score":0.9999837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01198030666410708,"score_gpt":0.2391882877123993,"score_spread":0.2272079810482922,"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."}}