{"id":"W3124788326","doi":"10.1364/oe.417396","title":"Bayesian framework for THz-TDS plasma diagnostics","year":2021,"lang":"en","type":"article","venue":"Optics Express","topic":"Terahertz technology and applications","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Achievement Rewards for College Scientists Foundation; Georgia Institute of Technology; National Science Foundation","keywords":"Terahertz radiation; Optics; Plasma; Plasma diagnostics; Physics; Refraction; Collision frequency; Noise (video); Spectroscopy; Measure (data warehouse); Bayesian probability; Plasma parameters; Terahertz spectroscopy and technology; Computation; Computational physics; Computer science; Algorithm; Artificial intelligence; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002284133,0.00009595838,0.0001030221,0.00002366494,0.00007408932,0.00002989498,0.0001522641,0.0002107757,0.00003840283],"category_scores_gemma":[0.0001395796,0.0001096489,0.0000406317,0.000104965,0.0000313062,0.00003424339,0.00003502943,0.0001796666,0.00002304794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001431201,"about_ca_system_score_gemma":0.000009789318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.453613e-7,"about_ca_topic_score_gemma":0.000001784959,"domain_scores_codex":[0.9995099,0.000003825597,0.0001157706,0.0001336968,0.00004826705,0.000188501],"domain_scores_gemma":[0.9992206,0.0003301579,0.00001332669,0.0003521018,0.0000401527,0.00004366542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005486061,0.0001343471,0.0004549179,0.0001438586,0.0001180817,0.00002875181,0.0004221157,0.009753116,0.01280014,0.916871,0.01651623,0.04275194],"study_design_scores_gemma":[0.000437752,0.00003293583,0.0001907441,0.0001123784,0.00006725603,0.00001973543,0.0002219062,0.1333239,0.2817078,0.1330262,0.4503535,0.0005058623],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02810481,0.0003829261,0.966715,0.0004309507,0.0003453701,0.0001524125,0.00007659986,0.0005141302,0.003277823],"genre_scores_gemma":[0.6563444,0.0002924689,0.3426493,0.00006394411,0.0001286165,0.0002205248,0.00003301845,0.00003696603,0.0002307994],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7838448,"threshold_uncertainty_score":0.4471352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01006402056821589,"score_gpt":0.2395428428086481,"score_spread":0.2294788222404322,"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."}}