{"id":"W2152519332","doi":"10.1109/isspa.2007.4555406","title":"Lossless source coding using repeat-accumulate codes","year":2007,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Lossless compression; Convolutional code; Algorithm; Data compression ratio; Block code; Compression (physics); Code rate; Linear code; Code (set theory); Concatenated error correction code; Theoretical computer science; Data compression; Decoding methods; Image compression; Artificial intelligence; 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":[0.0008187746,0.0005427227,0.000326854,0.0005312364,0.0002094754,0.0005533044,0.0005838947,0.000530554,0.0007197655],"category_scores_gemma":[0.003923725,0.0001741699,0.000224612,0.0005136878,0.0004591313,0.0008073439,0.0004954963,0.0004412935,0.0003767923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006672522,"about_ca_system_score_gemma":0.0004050303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001262178,"about_ca_topic_score_gemma":0.001113622,"domain_scores_codex":[0.9995015,0.0001468943,0.00002201744,0.00004899011,0.0002425264,0.00003817516],"domain_scores_gemma":[0.9982614,0.0007793744,0.0002502323,0.0003132859,0.0003641768,0.00003150433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004123948,0.00006055055,0.001279081,0.0002200793,0.00006721172,0.0003393313,0.0002119877,0.6727875,0.07958109,0.1097214,0.001483339,0.1338359],"study_design_scores_gemma":[0.00002038787,0.00007249377,0.0002921304,0.00002693644,0.00001373238,0.0001253487,0.00000652113,0.9465979,0.0393732,0.01171638,0.00173682,0.0000181439],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08255939,0.0004353265,0.9108664,0.0001831646,0.00003510718,0.00006428884,0.0001035078,0.001112447,0.004640368],"genre_scores_gemma":[0.7991477,0.0004501358,0.1967541,0.00006566866,0.00003552519,0.00008422667,0.0001219066,0.00007213061,0.00326863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001262178,"threshold_uncertainty_score":0.004841328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04782711716561696,"score_gpt":0.327624385557155,"score_spread":0.279797268391538,"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."}}