{"id":"W4301331999","doi":"10.48550/arxiv.1708.06012","title":"Product Matrix Minimum Storage Regenerating Codes with Flexible Number\\n of Helpers","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Distributed data store; Computer science; Coding (social sciences); Computer data storage; Distributed computing; Code (set theory); Computer network; Mathematics; Computer hardware; Set (abstract data type)","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":[0.001201733,0.0005779319,0.0006526368,0.0006400528,0.0005021232,0.0007605206,0.001028448,0.0005064032,0.001412982],"category_scores_gemma":[0.004194471,0.0002407904,0.0003306214,0.0009030694,0.001057904,0.001251089,0.001234089,0.001007293,0.0005336722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007176959,"about_ca_system_score_gemma":0.0009641566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008856099,"about_ca_topic_score_gemma":0.00116732,"domain_scores_codex":[0.9992199,0.0002480446,0.0000551244,0.0001292939,0.0002442183,0.0001034221],"domain_scores_gemma":[0.9958396,0.001856868,0.0004094329,0.0009870038,0.0007190504,0.0001881115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001273271,0.0001658265,0.001158003,0.0003690153,0.00006184804,0.0002933246,0.0005930991,0.3525132,0.06681705,0.3900299,0.004680579,0.1820449],"study_design_scores_gemma":[0.00005410538,0.0002740355,0.0002062477,0.00003464951,0.00002056403,0.0003731025,0.00005055455,0.889429,0.02465025,0.08056566,0.004298078,0.00004368811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09172624,0.0005525851,0.9021988,0.0001830441,0.00005423508,0.00008085529,0.0001445244,0.0006836425,0.004376059],"genre_scores_gemma":[0.6877546,0.0004442974,0.3051753,0.0001461385,0.00005736827,0.000221284,0.0003192603,0.0001679799,0.005713736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001412982,"threshold_uncertainty_score":0.006355464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.064492864889483,"score_gpt":0.233569269003929,"score_spread":0.169076404114446,"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."}}