{"id":"W4387736264","doi":"10.36227/techrxiv.24328879.v1","title":"Ordered Reliability Direct Error Pattern Testing Decoding Algorithm","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Decoding methods; Algorithm; Reliability (semiconductor); Computer science; Sequential decoding; List decoding; Product (mathematics); Berlekamp–Welch algorithm; Variety (cybernetics); Binary number; State (computer science); Order (exchange); Concatenated error correction code; Mathematics; Block code; Arithmetic; Artificial intelligence; Power (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":[0.001505154,0.0006774444,0.0008096807,0.001030848,0.0003050579,0.001018428,0.001727607,0.0008701266,0.002731262],"category_scores_gemma":[0.007489883,0.0002812969,0.0003854989,0.0009552417,0.0009378514,0.001202796,0.001432644,0.001250985,0.001315165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005240011,"about_ca_system_score_gemma":0.001828306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001439628,"about_ca_topic_score_gemma":0.001204372,"domain_scores_codex":[0.9979277,0.0005624695,0.0001553378,0.0003116679,0.0008966859,0.0001461057],"domain_scores_gemma":[0.9954036,0.001565427,0.0003163036,0.001106814,0.001463116,0.0001446062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000669613,0.0001260877,0.003176318,0.0002212407,0.00006393866,0.0002565977,0.0001610771,0.1376087,0.02178945,0.07615287,0.005909554,0.7538646],"study_design_scores_gemma":[0.0001017087,0.0001995839,0.00060958,0.00002776105,0.00002302864,0.000506152,0.00002774856,0.9233137,0.02648431,0.04289809,0.005760881,0.0000474062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.013788,0.0001204805,0.9827026,0.0001222935,0.00004476763,0.0000857277,0.0001583279,0.0008733866,0.002104271],"genre_scores_gemma":[0.2490923,0.0001568036,0.7450862,0.0001976483,0.00005700865,0.0002890207,0.0006747308,0.0001619679,0.004284339],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002731262,"threshold_uncertainty_score":0.009136975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06243711614455985,"score_gpt":0.3092117905610186,"score_spread":0.2467746744164587,"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."}}