{"id":"W2949161139","doi":"10.48550/arxiv.1308.1259","title":"On Characterization of Elementary Trapping Sets of Variable-Regular LDPC Codes","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Low-density parity-check code; Tanner graph; Node (physics); Variable (mathematics); Graph; Computer science; Set (abstract data type); Characterization (materials science); Coding (social sciences); Simple (philosophy); Code (set theory); Class (philosophy); Mathematics; Discrete mathematics; Combinatorics; Error floor; Algorithm; Decoding methods; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003362081,0.0002658996,0.0004255324,0.0004218618,0.00005894046,0.00002994505,0.001542723,0.0002314087,0.00004050787],"category_scores_gemma":[0.00003214901,0.0003195772,0.0001423106,0.0005526223,0.00008466258,0.0003485763,0.001043101,0.0003483404,0.000006535118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001299133,"about_ca_system_score_gemma":0.0001329448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003086579,"about_ca_topic_score_gemma":0.000006221542,"domain_scores_codex":[0.9983839,0.0001738893,0.0003438263,0.0007281463,0.0001390776,0.0002311275],"domain_scores_gemma":[0.9976577,0.0001188515,0.0007138728,0.001179937,0.0002620224,0.00006758071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001106561,0.0008089321,0.007766851,0.001317232,0.0005457537,0.00007914626,0.00180751,0.05530839,0.1850777,0.7414305,0.0005095888,0.005237678],"study_design_scores_gemma":[0.0006601966,0.0004223832,0.006573228,0.001569013,0.0001769779,0.000004962197,0.00008634029,0.719428,0.1365634,0.1334181,0.0001115413,0.0009858414],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5283733,0.000004395365,0.4704553,0.00001893934,0.0002093855,0.0002590219,0.00001836426,0.0001721302,0.0004891444],"genre_scores_gemma":[0.9889165,0.00004863564,0.01072498,0.00005592975,0.0000171446,0.000001847601,0.00005101485,0.00001755813,0.0001664532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6641197,"threshold_uncertainty_score":0.9999256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04591237933680283,"score_gpt":0.1890738693604568,"score_spread":0.1431614900236539,"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."}}