{"id":"W2950033884","doi":"10.1109/tit.2019.2923198","title":"Asymptotic Average Multiplicity of Structures Within Different Categories of Trapping Sets, Absorbing Sets, and Stopping Sets in Random Regular and Irregular LDPC Code Ensembles","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Information Theory","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Low-density parity-check code; Mathematics; Multiplicity (mathematics); Tanner graph; Code (set theory); Infinity; Block (permutation group theory); Combinatorics; Discrete mathematics; Trapping; Decoding methods; Algorithm; Error floor; Set (abstract data type); Computer science; Geometry; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001378197,0.0004132594,0.0006115888,0.001294717,0.0007153088,0.001017693,0.001033384,0.000779365,0.001051118],"category_scores_gemma":[0.0139214,0.0004703989,0.0005102149,0.0005926334,0.001953425,0.002099267,0.001374974,0.0008889138,0.0001479067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168517,"about_ca_system_score_gemma":0.0005414542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006361685,"about_ca_topic_score_gemma":0.0008409006,"domain_scores_codex":[0.9991295,0.0002228129,0.00005835316,0.0002092617,0.0002572738,0.0001227798],"domain_scores_gemma":[0.9900544,0.005979039,0.001392274,0.001022643,0.0008868828,0.0006647249],"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":[0.0007106846,0.0002414547,0.0562833,0.0003298475,0.0002192881,0.001405751,0.0006717041,0.5377399,0.07250088,0.3023299,0.00200426,0.02556292],"study_design_scores_gemma":[0.00001934757,0.0001077889,0.008974389,0.0000341205,0.00003939114,0.0006123143,0.00010007,0.9217582,0.01071993,0.05709143,0.0004834639,0.00005960496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9151623,0.0004282945,0.08022407,0.0002085049,0.00001963363,0.00002167114,0.0001735275,0.000210506,0.003551468],"genre_scores_gemma":[0.9920619,0.0001651558,0.006679317,0.00002806039,0.0000252713,0.00004058689,0.0002171655,0.0000492844,0.0007332644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001378197,"threshold_uncertainty_score":0.008478224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01341138576530478,"score_gpt":0.2421701643216751,"score_spread":0.2287587785563703,"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."}}