{"id":"W2757478948","doi":"10.1587/transcom.2017ebp3015","title":"Low-Latency Communication in LTE and WiFi Using Spatial Diversity and Encoding Redundancy","year":2017,"lang":"en","type":"article","venue":"IEICE Transactions on Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Redundancy (engineering); Antenna diversity; Latency (audio); Computer network; Encoding (memory); Telecommunications; Wireless; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006489256,0.0004725282,0.0002882858,0.0003472114,0.0004598656,0.0005738673,0.0006071954,0.0004683832,0.0004874827],"category_scores_gemma":[0.002058604,0.0001399553,0.0002135749,0.0005238036,0.0005164342,0.0009188849,0.000558776,0.0003304452,0.00008900434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007950217,"about_ca_system_score_gemma":0.0006333581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003055351,"about_ca_topic_score_gemma":0.003544953,"domain_scores_codex":[0.9994436,0.0001523479,0.00002156656,0.00006092636,0.0002090079,0.0001125263],"domain_scores_gemma":[0.9989929,0.0004962042,0.0001852446,0.0001067106,0.0001893854,0.00002961765],"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":[0.0006953144,0.0001076286,0.003960545,0.0002746187,0.00007990623,0.00100843,0.0004234884,0.6430992,0.1107754,0.05868495,0.001319319,0.1795712],"study_design_scores_gemma":[0.00001813032,0.0002985235,0.0007139536,0.00002321913,0.00003774081,0.0004083509,0.00009111159,0.9648547,0.02565511,0.006099197,0.001769802,0.00003014567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.330149,0.001516411,0.6621256,0.0003538518,0.0000547926,0.00005285795,0.00006633718,0.000375337,0.005305854],"genre_scores_gemma":[0.9816058,0.0002230091,0.01757921,0.00002436485,0.00001235997,0.0000158336,0.00001522435,0.0000061206,0.0005179251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003055351,"threshold_uncertainty_score":0.006075144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09279263198215977,"score_gpt":0.3181030689530498,"score_spread":0.22531043697089,"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."}}