{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002713677,0.0004284915,0.0005725361,0.000251149,0.0003113524,0.0001569365,0.004451185,0.0002472569,0.00000959106],"category_scores_gemma":[0.0001634788,0.0004366433,0.0001245965,0.0004368696,0.0005316703,0.000978714,0.004202243,0.0006431525,0.00003090656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002056392,"about_ca_system_score_gemma":0.0003384231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001744042,"about_ca_topic_score_gemma":0.00004332224,"domain_scores_codex":[0.9975203,0.00008178272,0.0002484681,0.001540512,0.0001728209,0.0004361316],"domain_scores_gemma":[0.9944478,0.00007794005,0.0008690055,0.00423091,0.0002778831,0.00009647454],"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":[0.0001154744,0.0001931179,0.009098295,0.0005408914,0.0002808619,0.0009579138,0.0005046579,0.5001981,0.001144056,0.4825961,0.0008275034,0.003543094],"study_design_scores_gemma":[0.004405518,0.0008345066,0.002176885,0.00314654,0.0006185393,0.0002960717,0.001997045,0.6600406,0.121202,0.1949926,0.003696201,0.006593529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3306874,0.0002812118,0.6655188,0.0001297309,0.0003497709,0.0004000592,0.00008006131,0.0008195363,0.001733416],"genre_scores_gemma":[0.9081293,0.0001709757,0.08964039,0.00001074618,0.00005040802,0.000002197476,0.0000241392,0.00002739499,0.001944499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5774418,"threshold_uncertainty_score":0.9998085,"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."}}