{"id":"W4238310787","doi":"10.1002/9781118694077.ch3","title":"Decoding","year":2014,"lang":"en","type":"other","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Decoding methods; A priori and a posteriori; Key (lock); Computer science; Maximum a posteriori estimation; Algorithm; Maximum likelihood; Mathematics; Statistics; Philosophy; Computer security","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001292098,0.0001255177,0.0001385463,0.0001950105,0.00001599471,0.0000610016,0.001029888,0.0001373045,0.0005956714],"category_scores_gemma":[0.00002834061,0.0001110184,0.00004230446,0.0001111064,0.000009691319,0.00003221625,0.0002543935,0.0001111568,0.0008778704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001592494,"about_ca_system_score_gemma":0.00001687993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001010878,"about_ca_topic_score_gemma":0.0001134035,"domain_scores_codex":[0.9993399,0.00002344897,0.00008046677,0.0002905024,0.0001207615,0.0001449066],"domain_scores_gemma":[0.9991184,0.00003315763,0.00008303166,0.0007203383,0.000009691146,0.00003530891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[2.406281e-8,0.000002184253,0.00001335259,0.000006564239,0.000002819331,0.000001473037,0.000007856353,3.176813e-8,0.000006512289,0.03397784,0.9400924,0.02588895],"study_design_scores_gemma":[0.00001918058,0.00001142152,0.000002218143,0.00007419899,0.000001401144,0.0000059948,4.21754e-7,0.002534612,0.0002427925,0.00117992,0.995755,0.0001728222],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[5.31839e-8,0.00003282964,0.4905135,0.00004787459,0.0003177796,0.00003724643,6.943578e-8,0.003307927,0.5057427],"genre_scores_gemma":[0.00009747656,0.00000729262,0.3599312,0.0002478926,0.0001120112,0.000004886549,3.24046e-7,0.0001114675,0.6394874],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1337447,"threshold_uncertainty_score":0.9999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134023800903569,"score_gpt":0.2532283741451989,"score_spread":0.2418881361361632,"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."}}