{"id":"W1837551539","doi":"10.26421/qic12.3-4-2","title":"Improved Data Post-Processing in Quantum Key Distribution and Application to Loss Thresholds in device independent QKD","year":2012,"lang":"en","type":"article","venue":"Quantum Information and Computation","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quantum key distribution; Computer science; Key (lock); Binary number; Scheme (mathematics); Transmittance; Algorithm; Mathematical proof; Quantum; Mathematics; Optics; Arithmetic; Computer security; Physics; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001127739,0.0001890118,0.0001845418,0.0005000545,0.000158021,0.0004223587,0.0003910291,0.0001152613,0.000001669111],"category_scores_gemma":[0.00006612829,0.000191397,0.00001830451,0.0009911393,0.00003832215,0.009523412,0.0003208686,0.0001876631,0.00003093858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008497494,"about_ca_system_score_gemma":0.00007059581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001874802,"about_ca_topic_score_gemma":0.00008572382,"domain_scores_codex":[0.9982442,0.00006106013,0.0007512101,0.000242996,0.000320413,0.0003800812],"domain_scores_gemma":[0.9989763,0.00004328496,0.0002814614,0.0003338688,0.0001760558,0.0001890215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000128603,0.0002272967,0.03375958,0.0003642259,0.000009613906,9.652839e-7,0.02278728,0.003359774,0.0002964223,0.2835643,0.0002474252,0.6552545],"study_design_scores_gemma":[0.000633289,0.00005428384,0.1601796,0.000037324,0.000003194837,0.0000202033,0.001028222,0.8349933,0.00003236021,0.001158555,0.001643867,0.000215832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.390726,0.00009194087,0.6077996,0.0007513527,0.0001012185,0.0003542394,0.00002170182,0.00007593673,0.00007794274],"genre_scores_gemma":[0.9952512,0.00004032188,0.002654111,0.001167627,0.00002271313,0.0000453612,0.000812726,0.000005350681,5.656442e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8316335,"threshold_uncertainty_score":0.7804942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01705356756772208,"score_gpt":0.2781427047566181,"score_spread":0.261089137188896,"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."}}