{"id":"W4403023799","doi":"10.1109/bdai62182.2024.10692447","title":"Machine Learning for Polar Codes in Small IoT Devices","year":2024,"lang":"en","type":"article","venue":"","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Internet of Things; Polar; Computer security; Physics","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":[0.0003643621,0.0002132534,0.0002007898,0.0002523833,0.0002359135,0.0003861168,0.0003259047,0.0003487298,0.001012402],"category_scores_gemma":[0.002070881,0.0001168756,0.0001688153,0.0001909426,0.0006178237,0.0006387082,0.0003839435,0.0005084806,0.0001909819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004819492,"about_ca_system_score_gemma":0.0004250181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009476773,"about_ca_topic_score_gemma":0.001071538,"domain_scores_codex":[0.9998442,0.00004690171,0.000006823043,0.00002822283,0.00005781005,0.00001606814],"domain_scores_gemma":[0.9992681,0.0005060652,0.00005930095,0.00004467963,0.0001027199,0.00001917312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001670276,0.00007669999,0.002112324,0.0001822936,0.00003163115,0.0001966625,0.0001276911,0.7224138,0.03042488,0.1475476,0.001410189,0.0953092],"study_design_scores_gemma":[0.000004230807,0.00002328028,0.0001298608,0.000005690194,0.00000222716,0.00001907674,0.000007923086,0.9846991,0.00345187,0.011083,0.0005700133,0.000003806646],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1151559,0.0005769471,0.8748188,0.0006022405,0.00007393217,0.000046283,0.00004320543,0.0002729527,0.00840976],"genre_scores_gemma":[0.8887467,0.0004032382,0.1069287,0.0001197198,0.00003195429,0.00007258006,0.00005679689,0.00002534266,0.003615043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001012402,"threshold_uncertainty_score":0.003496766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02051383924998931,"score_gpt":0.2701058753806214,"score_spread":0.2495920361306321,"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."}}