{"id":"W2564997090","doi":"10.1103/physrevb.91.205407","title":"Optimizing third-harmonic generation at terahertz frequencies in graphene","year":2015,"lang":"en","type":"article","venue":"Physical Review B","topic":"Terahertz technology and applications","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Physics; Amplitude; Scattering; Terahertz radiation; High harmonic generation; Condensed matter physics; Graphene; Harmonic; Fermi energy; Scattering amplitude; Fermi level; Optics; Quantum mechanics; Electron","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":[0.00008228025,0.0001042223,0.0001888112,0.00003063991,0.00002715887,0.000007485499,0.0001213541,0.0000294061,0.000009041529],"category_scores_gemma":[0.00002506973,0.00009335656,0.00005179774,0.0002612405,0.00002905624,0.000110603,0.0000276074,0.000143884,0.0001775035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008592197,"about_ca_system_score_gemma":0.000007948662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006945647,"about_ca_topic_score_gemma":0.00002119611,"domain_scores_codex":[0.9994724,0.00001762499,0.0001527478,0.0001316573,0.00007779649,0.000147764],"domain_scores_gemma":[0.9996746,0.00001799756,0.0000195322,0.0002197812,0.00001850835,0.00004961536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006626469,0.0003800304,0.001040643,0.001261632,0.000105363,0.00002078197,0.001768598,0.003675884,0.4751093,0.04847955,0.04144388,0.4267077],"study_design_scores_gemma":[0.001402175,0.000146479,0.002655288,0.002043543,0.0001935941,0.0000351341,0.00006521579,0.1940316,0.203322,0.04038125,0.5540234,0.001700277],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9326589,0.05993403,0.001712343,0.001168402,0.0001000309,0.0003831189,0.000004242047,0.0005575477,0.003481368],"genre_scores_gemma":[0.9897598,0.00831105,0.001364865,0.0002381852,0.0000799605,0.0001931062,0.00001433444,0.0000156089,0.00002305928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5125796,"threshold_uncertainty_score":0.380697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04378981806903229,"score_gpt":0.2836524942956584,"score_spread":0.2398626762266261,"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."}}