{"id":"W3122317806","doi":"10.3390/nano11020283","title":"Homodyne Solid-State Biased Coherent Detection of Ultra-Broadband Terahertz Pulses with Static Electric Fields","year":2021,"lang":"en","type":"article","venue":"Nanomaterials","topic":"Terahertz technology and applications","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Homodyne detection; Direct-conversion receiver; Terahertz radiation; Heterodyne (poetry); Physics; Heterodyne detection; Demodulation; Broadband; Optics; SIGNAL (programming language); Biasing; Dynamic range; Electronic engineering; Voltage; Electrical engineering; Engineering; Computer science; Channel (broadcasting); Acoustics; Laser","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":[0.0001232518,0.0001870688,0.0001724623,0.0001969883,0.0001223809,0.0003088126,0.0003421153,0.0003231341,0.0007629058],"category_scores_gemma":[0.0002499533,0.0001111555,0.0000583913,0.0002399677,0.0003929662,0.0004258653,0.0003573329,0.0003069071,0.0002034805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002382969,"about_ca_system_score_gemma":0.0002098393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001928024,"about_ca_topic_score_gemma":0.0005785784,"domain_scores_codex":[0.9998821,0.0000147217,0.00000475116,0.00002935616,0.00005244372,0.00001661387],"domain_scores_gemma":[0.9998882,0.00003285256,0.00002889267,0.0000191144,0.00001953248,0.00001143972],"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":[0.00002816216,0.00001985492,0.0001785268,0.00004220687,0.000003345843,0.00007346262,0.00003385272,0.0003458794,0.987888,0.002188544,0.00006871143,0.00912944],"study_design_scores_gemma":[0.000005586035,0.00005780885,0.0003256699,0.000004469957,0.000003410151,0.0001174714,0.00001282318,0.005197894,0.9925443,0.0003359373,0.001388878,0.000005762646],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9173499,0.001587256,0.07307247,0.0002684367,0.00009145719,0.00004425758,0.0001223947,0.00013754,0.00732627],"genre_scores_gemma":[0.9552689,0.0007333067,0.04091692,0.0001112854,0.0000299161,0.00002460728,0.00006833994,0.00001055455,0.002836181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007629058,"threshold_uncertainty_score":0.002552211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006497501373607393,"score_gpt":0.2146061768999429,"score_spread":0.2081086755263355,"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."}}