{"id":"W4212901485","doi":"10.1109/icaml54311.2021.00033","title":"Software and Hardware Integrated Accelerators for Hadoop Appliance","year":2021,"lang":"en","type":"article","venue":"2021 3rd International Conference on Applied Machine Learning (ICAML)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Throughput; Scalability; Computer hardware; Field-programmable gate array; Data compression; Lossless compression; Software; Embedded system; Hardware acceleration; Hardware architecture; Computer architecture; Operating system; Wireless","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":[0.0003340849,0.0004162984,0.0002426436,0.0004838737,0.0003154913,0.0006282934,0.001117433,0.0002769592,0.004816909],"category_scores_gemma":[0.0008049721,0.0002222417,0.0003008928,0.0004852456,0.0001763675,0.000788489,0.0004011738,0.0005971449,0.001080225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004931072,"about_ca_system_score_gemma":0.0009050075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001389069,"about_ca_topic_score_gemma":0.001816612,"domain_scores_codex":[0.9997126,0.00002696294,0.00001890322,0.00004438169,0.0001579415,0.00003918124],"domain_scores_gemma":[0.9995977,0.00006851413,0.00003364981,0.00007455219,0.0001924378,0.000033212],"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":[0.00177783,0.0004045092,0.006301759,0.0007149046,0.0001999473,0.0006035463,0.0003073026,0.0465468,0.2841191,0.02611378,0.0421241,0.5907864],"study_design_scores_gemma":[0.0003468755,0.001346443,0.00512761,0.0000920153,0.0001537748,0.0008949621,0.0001539155,0.6273009,0.2550832,0.009894799,0.09947801,0.0001275809],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1236142,0.001196208,0.8338091,0.0006281175,0.0006972726,0.0004758064,0.0005652467,0.01830834,0.02070569],"genre_scores_gemma":[0.6550691,0.0003914442,0.3295828,0.000285939,0.0001207648,0.0002736922,0.0009599627,0.000395717,0.01292057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004816909,"threshold_uncertainty_score":0.01611418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03219858722064655,"score_gpt":0.2799681508879874,"score_spread":0.2477695636673409,"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."}}