{"id":"W1948599384","doi":"10.1109/tit.2015.2503766","title":"A Model for Adversarial Wiretap Channels","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Secrecy; Computer science; Eavesdropping; Code word; Code (set theory); Channel (broadcasting); Reliability (semiconductor); Theoretical computer science; Adversarial system; Algorithm; Computer network; Artificial intelligence; Decoding methods; Power (physics); Computer security","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":[0.0003940026,0.0001226571,0.0001139898,0.0002115709,0.00008761765,0.00004926259,0.0002090102,0.0001107837,0.00001875849],"category_scores_gemma":[0.00001070488,0.0001336658,0.0000778053,0.0001212717,0.00003464943,0.001172352,9.79198e-7,0.0001644524,0.00009379282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001198813,"about_ca_system_score_gemma":0.0000400432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002322981,"about_ca_topic_score_gemma":0.000002627066,"domain_scores_codex":[0.9993441,0.00002676364,0.0002826649,0.00005305597,0.0001492112,0.0001441728],"domain_scores_gemma":[0.9993302,0.0000833992,0.00004164075,0.0003259907,0.0001344272,0.00008429138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009902097,0.00002141092,5.068094e-8,0.00003125046,0.0000293141,2.782913e-8,0.007056961,0.9535851,0.00006301639,0.01321925,0.001996098,0.02389854],"study_design_scores_gemma":[0.0005823974,0.0000375775,2.047679e-7,0.00001509911,0.00001421569,0.000001807001,0.0003085689,0.9670967,0.01335209,0.01450075,0.003936109,0.0001544843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001228045,0.00001349696,0.9927372,0.00006988455,0.0005395771,0.0004007553,0.00008272906,0.001068945,0.003859309],"genre_scores_gemma":[0.9914653,0.00002511709,0.007759885,0.0001911517,0.00002598198,0.0003641177,0.00002317406,0.00001990951,0.0001254174],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9902372,"threshold_uncertainty_score":0.5450732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03398889484881486,"score_gpt":0.2506278560638254,"score_spread":0.2166389612150105,"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."}}