{"id":"W1529567625","doi":"10.1007/978-3-642-04159-4_2","title":"Efficient Pairing Computation on Genus 2 Curves in Projective Coordinates","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cryptography and Residue Arithmetic","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperelliptic curve cryptography; Elliptic curve; Pairing; Supersingular elliptic curve; Computation; Homogeneous coordinates; Mathematics; Finite field; Hyperelliptic curve; Edwards curve; Cryptography; Prime (order theory); Genus; Context (archaeology); Schoof's algorithm; Pure mathematics; Computer science; Discrete mathematics; Algorithm; Elliptic curve cryptography; Combinatorics; Public-key cryptography; Quarter period","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009697002,0.0005296432,0.0005407786,0.001863483,0.0002213564,0.000302423,0.001805064,0.0002430359,0.000003446956],"category_scores_gemma":[0.00008498563,0.0004701619,0.000155552,0.0014864,0.0004714718,0.0001935508,0.0005090435,0.0009342246,0.00001975451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003189929,"about_ca_system_score_gemma":0.0003207748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003919048,"about_ca_topic_score_gemma":0.00007129143,"domain_scores_codex":[0.9961572,0.00007626537,0.000528537,0.001587106,0.0009737362,0.0006771673],"domain_scores_gemma":[0.9980708,0.0006119549,0.0002605417,0.0007427372,0.0001933787,0.0001205458],"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":[0.000007437322,0.00008904398,0.00008044739,0.00006569246,0.000006486922,0.0001331021,0.0006751964,0.3269126,0.0000199048,0.006130096,0.00001138152,0.6658686],"study_design_scores_gemma":[0.0004761015,0.0005400011,0.003775757,0.003208164,0.000008188659,0.00005935189,4.711135e-7,0.9102591,0.0006551402,0.07999921,0.0001804593,0.00083801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001178986,0.001552257,0.9912937,0.001081762,0.0008525066,0.0009996746,0.000002743975,0.0001728517,0.002865484],"genre_scores_gemma":[0.8314157,0.0001586614,0.165398,0.002600683,0.000259154,0.00003465166,0.000007185063,0.00004134676,0.00008454399],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8302367,"threshold_uncertainty_score":0.999775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01330183687860362,"score_gpt":0.2465152601712274,"score_spread":0.2332134232926237,"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."}}