{"id":"W2037957312","doi":"10.1109/chinacom.2011.6158162","title":"On the capacity of finite-state channels with noisy state information at the encoder","year":2011,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Encoder; Finite state; State (computer science); Computer science; Process (computing); Finite-state machine; State information; Upper and lower bounds; Decoding methods; Algorithm; Mathematics; Mathematical analysis; Markov chain","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":[0.000224466,0.00008928968,0.00007747141,0.000042646,0.0000628719,0.00001595345,0.0003025419,0.00002478853,0.0001878928],"category_scores_gemma":[0.00002324895,0.00004459915,0.00002176047,0.0001157517,0.000085999,0.0002346593,0.00005997446,0.0001447338,0.0000571877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002869161,"about_ca_system_score_gemma":0.000005013861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002123961,"about_ca_topic_score_gemma":0.000261919,"domain_scores_codex":[0.9995027,0.00003689315,0.0001783194,0.00003877846,0.0001382672,0.0001050634],"domain_scores_gemma":[0.999115,0.0001829855,0.000058784,0.0005538593,0.00007120378,0.00001815809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004804642,0.0003372513,0.00149574,0.0005218342,0.0006639197,0.000003722635,0.4653617,0.2307867,0.005481289,0.228469,0.03927719,0.02712132],"study_design_scores_gemma":[0.000339549,0.000191022,0.001646181,0.0000976672,0.00001458378,0.00000563891,0.0007304676,0.1027485,0.8687869,0.01303293,0.01201172,0.0003949041],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8965483,0.00001305338,0.05470653,0.0002528787,0.00003413935,0.0003634215,0.00002447842,0.0003683681,0.04768885],"genre_scores_gemma":[0.998639,0.00006740047,0.0009129165,0.0001950989,0.000002122903,0.0000491002,0.000004880406,0.00000996832,0.0001194664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8633056,"threshold_uncertainty_score":0.2057295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02849910053805145,"score_gpt":0.1943686052751658,"score_spread":0.1658695047371143,"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."}}