{"id":"W4243498042","doi":"10.20944/preprints201811.0522.v1","title":"Contactless In-Situ Electrical Characterization Method of Printed Electronic Devices with Terahertz Spectroscopy","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Terahertz technology and applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Terahertz radiation; Characterization (materials science); Inkwell; Materials science; Printed electronics; Optoelectronics; Electronics; 3D printing; Electronic component; Conductive ink; Nanotechnology; Electrical engineering; Engineering; Sheet resistance","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":[0.0003606548,0.0006114438,0.0005478097,0.0006353935,0.0003003411,0.0009305829,0.0009452857,0.0008376808,0.002069811],"category_scores_gemma":[0.001205953,0.0003981932,0.0003087049,0.0005706522,0.0006677907,0.0009030062,0.0007577167,0.0009754461,0.0008106067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003135241,"about_ca_system_score_gemma":0.000171171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001433931,"about_ca_topic_score_gemma":0.0002984861,"domain_scores_codex":[0.9988807,0.0001314994,0.000063468,0.000299835,0.0005645952,0.00005999284],"domain_scores_gemma":[0.9988598,0.0003525748,0.0002417087,0.0003374594,0.0001689072,0.00003951804],"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":[0.00001810976,0.00001585157,0.0001420998,0.00007024282,0.00000584094,0.00005937402,0.00003990192,0.0001502989,0.9956326,0.0002780996,0.00007635707,0.003511118],"study_design_scores_gemma":[0.000002053062,0.00002163291,0.0003193792,0.000002604259,0.00000548779,0.00009256948,0.00001305016,0.001717483,0.9969452,0.0001091245,0.0007665513,0.000004991808],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4702292,0.002468859,0.5170824,0.0004236524,0.0004782952,0.0002128564,0.0005904378,0.001493872,0.007020459],"genre_scores_gemma":[0.8514513,0.001134887,0.141514,0.0001715333,0.00007857067,0.000163634,0.0002305553,0.0002017232,0.005053796],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002069811,"threshold_uncertainty_score":0.006924152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02945713612500142,"score_gpt":0.3144181080194656,"score_spread":0.2849609718944641,"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."}}