{"id":"W4390691092","doi":"10.1109/tvlsi.2023.3348809","title":"Design of a Stochastic Computing Architecture for the Phansalkar Algorithm","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Stochastic computing; Computer science; Algorithm; Key (lock); Architecture; Stochastic process; Binary number; Mathematics; Arithmetic; Computation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001252988,0.0002895899,0.0003206651,0.0003836996,0.0003913015,0.0003362019,0.0007343711,0.0001547336,0.000004297322],"category_scores_gemma":[0.00002271906,0.0002094089,0.0002525804,0.0007520798,0.00006261605,0.0003295468,0.000005333993,0.0005092749,0.00001764399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001509882,"about_ca_system_score_gemma":0.0001444976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008972326,"about_ca_topic_score_gemma":0.00005814166,"domain_scores_codex":[0.9977904,0.0002714773,0.0005782171,0.0005486438,0.0004510676,0.0003601741],"domain_scores_gemma":[0.9971468,0.001752334,0.0001477536,0.0006360773,0.0002508582,0.0000662001],"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":[0.00005139882,0.0002416663,3.16923e-7,0.0002400306,0.0002079513,0.000006704605,0.009027112,0.6467049,0.0101232,0.001628232,0.0009348824,0.3308336],"study_design_scores_gemma":[0.0001908977,0.0002782687,7.577366e-7,0.0006391674,0.00005423907,0.00007899047,0.0004449316,0.9733298,0.0240793,0.0003906249,0.000306853,0.0002061976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001350443,0.0004753258,0.9914793,0.0002709047,0.004935513,0.00153124,0.00008343795,0.001060694,0.00002853597],"genre_scores_gemma":[0.9067588,0.000008269789,0.09229043,0.00005930616,0.0001615632,0.0003280332,0.00000329356,0.00004243746,0.0003478465],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9066238,"threshold_uncertainty_score":0.8539447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02181472295618142,"score_gpt":0.2745561485655515,"score_spread":0.2527414256093701,"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."}}