{"id":"W2157927886","doi":"10.1109/tit.2014.2334657","title":"On Characterization of Elementary Trapping Sets of Variable-Regular LDPC Codes","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Low-density parity-check code; Coding (social sciences); Computer science; Node (physics); Variable (mathematics); Set (abstract data type); Characterization (materials science); Tanner graph; Graph; Class (philosophy); Simple (philosophy); Code (set theory); Discrete mathematics; Combinatorics; Decoding methods; Mathematics; Theoretical computer science; Algorithm; Error floor; 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":[],"consensus_categories":[],"category_scores_codex":[0.0009543938,0.0001287584,0.0001779885,0.0004239775,0.0001042369,0.00002829942,0.0003753259,0.00007458586,0.00004501166],"category_scores_gemma":[0.00002076068,0.000129607,0.00007119749,0.000393558,0.00004804303,0.001319395,0.000002911942,0.0001425921,0.00001382234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004345967,"about_ca_system_score_gemma":0.00003766779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001067386,"about_ca_topic_score_gemma":8.712541e-7,"domain_scores_codex":[0.9987569,0.000172685,0.0005270225,0.0001180717,0.0002911155,0.0001341359],"domain_scores_gemma":[0.9987441,0.0002599057,0.000359724,0.0004487077,0.00015213,0.0000354296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002176873,0.0003332855,0.00001429042,0.0003164707,0.0001079665,1.77807e-7,0.006987732,0.01969649,0.1065348,0.4121604,0.0000857214,0.453545],"study_design_scores_gemma":[0.0004500146,0.0004628031,0.00023421,0.0002498607,0.00002288558,0.000005125704,0.0001169275,0.09054017,0.8864021,0.02100164,0.000295874,0.0002184052],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05388786,8.202049e-7,0.9436823,0.00005373625,0.0003648344,0.0002195704,0.00002599147,0.0002318008,0.001533125],"genre_scores_gemma":[0.9840318,0.0000056971,0.01551451,0.0003645541,0.000007055994,0.00002924911,0.00001564193,0.000007050049,0.00002445136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.930144,"threshold_uncertainty_score":0.5285221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006652840438808025,"score_gpt":0.2176007685741675,"score_spread":0.2109479281353595,"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."}}