{"id":"W2921327408","doi":"10.1109/access.2019.2903304","title":"Exploring Various Levels of Parallelism in High-Performance CRC Algorithms","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Higher Education Discipline Innovation Project","keywords":"Computer science; Parallel computing; Task parallelism; Thread (computing); Data parallelism; Instruction-level parallelism; Slicing; Speedup; Parallelism (grammar); Implicit parallelism; Algorithm; Computation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001366113,0.0005593368,0.0004286979,0.0007938543,0.000620791,0.001023957,0.0006736465,0.0005630691,0.00131436],"category_scores_gemma":[0.004456534,0.0003032578,0.0005056009,0.0008455318,0.0008509719,0.002346404,0.0007972733,0.0009652094,0.0002852023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007310974,"about_ca_system_score_gemma":0.001487847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001376892,"about_ca_topic_score_gemma":0.00215813,"domain_scores_codex":[0.9988155,0.0003117608,0.0000617578,0.0001406869,0.0005311104,0.0001391418],"domain_scores_gemma":[0.9980313,0.001057672,0.0002030465,0.0003265765,0.0003193733,0.00006196756],"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":[0.0004291629,0.0002718968,0.004093451,0.0003405779,0.00007658036,0.0003264563,0.0004241879,0.5144443,0.04473008,0.1791588,0.001739675,0.253965],"study_design_scores_gemma":[0.00003480765,0.0001986052,0.0004558341,0.00003149411,0.0000205362,0.0001417991,0.00005535396,0.9282308,0.01342827,0.05415665,0.003224107,0.00002177841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1152448,0.001399847,0.8737472,0.0004875438,0.00004973221,0.0001212787,0.00004029328,0.000497515,0.008411719],"genre_scores_gemma":[0.6544901,0.0007660426,0.3426332,0.0001074167,0.00005091838,0.00007959124,0.00007974373,0.00007768066,0.001715284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001376892,"threshold_uncertainty_score":0.007224739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09974725890309234,"score_gpt":0.2800559948137624,"score_spread":0.1803087359106701,"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."}}