{"id":"W4301303211","doi":"10.48550/arxiv.1308.1259","title":"On Characterization of Elementary Trapping Sets of Variable-Regular LDPC\\n Codes","year":2013,"lang":"","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; Node (physics); Tanner graph; Variable (mathematics); Graph; Set (abstract data type); Computer science; Characterization (materials science); Coding (social sciences); Simple (philosophy); Class (philosophy); Code (set theory); 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":[0.0009269672,0.0007019789,0.001074352,0.0009326007,0.0002002355,0.00007525425,0.002921466,0.0005540245,0.0002610746],"category_scores_gemma":[0.00008804913,0.000898831,0.0003884576,0.001621999,0.0003525627,0.0008849456,0.001911948,0.0008032164,0.00002600673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003551778,"about_ca_system_score_gemma":0.0003906683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007685734,"about_ca_topic_score_gemma":0.00001365728,"domain_scores_codex":[0.9957174,0.0005808097,0.0009984629,0.001771057,0.0003284773,0.0006037871],"domain_scores_gemma":[0.9941794,0.0003461904,0.002161802,0.002306954,0.0008062035,0.0001995116],"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":[0.0004603212,0.002114713,0.01084977,0.002633459,0.001251074,0.0001416127,0.004057242,0.1058889,0.3298934,0.5321509,0.0002743822,0.01028416],"study_design_scores_gemma":[0.0009897186,0.0008880341,0.006442833,0.002881367,0.0004060428,0.000009210812,0.0002106668,0.8070687,0.1271043,0.05256722,0.0001157252,0.001316204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5402192,0.000009810501,0.457651,0.00002836876,0.0005172935,0.0006659908,0.00005158056,0.0001683528,0.0006884749],"genre_scores_gemma":[0.9905574,0.0002670228,0.008451304,0.00008962595,0.00004287913,0.000003541705,0.00009333377,0.00004733482,0.0004475172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7011798,"threshold_uncertainty_score":0.9993463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05129862370862853,"score_gpt":0.1912948531553252,"score_spread":0.1399962294466966,"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."}}