{"id":"W3000658051","doi":"10.48550/arxiv.2001.02612","title":"Adaptive Coding for Two-Way Lossy Source-Channel Communication","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lossy compression; Channel code; Computer science; Coding (social sciences); Channel (broadcasting); Distributed source coding; Algorithm; Source code; Decoding methods; Theoretical computer science; Mathematics; Telecommunications; Artificial intelligence; Statistics","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":[0.000240877,0.0003817228,0.000447301,0.0002201034,0.0002028126,0.00007440809,0.001686458,0.0004130839,0.00001992304],"category_scores_gemma":[0.00004953741,0.0005397052,0.0002588882,0.0003089871,0.0001379615,0.0002079932,0.001356245,0.001110616,0.0000398847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003860002,"about_ca_system_score_gemma":0.00004659256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001117553,"about_ca_topic_score_gemma":0.00008225282,"domain_scores_codex":[0.9986762,0.0001405409,0.0002902118,0.0005172268,0.00007430014,0.0003014576],"domain_scores_gemma":[0.9974974,0.0003091527,0.0001949283,0.001637113,0.000211624,0.0001497438],"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":[0.00007764751,0.00005828044,0.00005910506,0.0003428126,0.000305693,0.000009845906,0.001641731,0.8748265,0.0003628861,0.1183291,0.003194591,0.0007918131],"study_design_scores_gemma":[0.0004571133,0.00003072329,0.00003373322,0.0002383549,0.0001034595,0.000001241177,0.0004090751,0.9522418,0.001701424,0.04156251,0.002672281,0.0005482203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02603264,0.0003946013,0.9646708,0.0001996796,0.0001799372,0.000986349,0.0001281008,0.00228759,0.00512027],"genre_scores_gemma":[0.9939193,0.001050396,0.004386524,0.00006384332,0.00007606055,0.00002044861,0.0002110156,0.0001032735,0.0001692104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9678866,"threshold_uncertainty_score":0.9997054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1107026368026087,"score_gpt":0.2097641546454815,"score_spread":0.09906151784287286,"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."}}