{"id":"W2733974844","doi":"10.1109/fccm.2017.42","title":"A Case for Common-Case: On FPGA Acceleration of Erasure Coding","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Erasure code; Computer science; Erasure; Decoding methods; Coding (social sciences); Reliability (semiconductor); Field-programmable gate array; Replication (statistics); Throughput; Reliability engineering; Embedded system; Power (physics); Operating system; Algorithm; Engineering","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":[0.0004896708,0.0005205431,0.000325955,0.0004747769,0.0003619574,0.0008010204,0.000724584,0.0004491947,0.004466225],"category_scores_gemma":[0.001059121,0.0001416822,0.0002848369,0.000493182,0.0005233141,0.001874733,0.00062014,0.0007107421,0.0009481767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005276801,"about_ca_system_score_gemma":0.0004297511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008976964,"about_ca_topic_score_gemma":0.002376772,"domain_scores_codex":[0.9995143,0.00007214172,0.00002344476,0.00006312213,0.0001788659,0.0001481211],"domain_scores_gemma":[0.9993466,0.0001553147,0.00005949844,0.0002166816,0.000173599,0.00004843763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001216218,0.0003005466,0.01080997,0.000700405,0.0001881585,0.002108092,0.0006541631,0.07646205,0.2908462,0.2191166,0.01355557,0.3840421],"study_design_scores_gemma":[0.0001235382,0.002112102,0.008288455,0.0003108102,0.0002230167,0.004288971,0.0004982223,0.2051353,0.572826,0.05102092,0.1550355,0.0001370864],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6315975,0.008291391,0.2674155,0.003651177,0.00061167,0.0000889633,0.0002126364,0.003371075,0.08476011],"genre_scores_gemma":[0.9446864,0.001245394,0.04526241,0.0002126479,0.0000761807,0.00002248436,0.00009088754,0.00009807849,0.008305588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004466225,"threshold_uncertainty_score":0.01494104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1021398524045299,"score_gpt":0.3450257762743207,"score_spread":0.2428859238697909,"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."}}