{"id":"W2809683060","doi":"10.14778/3231751.3231764","title":"Experimental analysis of distributed graph systems","year":2018,"lang":"en","type":"preprint","venue":"Proceedings of the VLDB Endowment","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"PageRank; Computer science; Scalability; Heuristics; Graph; Usability; SPARK (programming language); Distributed computing; Power graph analysis; Theoretical computer science; Database; Human–computer interaction; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0006656767,0.0003162929,0.0007323844,0.0004651828,0.0001085925,0.0001384584,0.00307182,0.0001474342,0.00001044005],"category_scores_gemma":[0.00003487631,0.000222702,0.0007460807,0.001533498,0.0002533142,0.0001533132,0.002966542,0.0002401729,0.000001728081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008163501,"about_ca_system_score_gemma":0.00004052169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008987523,"about_ca_topic_score_gemma":3.820164e-7,"domain_scores_codex":[0.997674,0.00002762121,0.0006973741,0.0006106374,0.0006958036,0.000294505],"domain_scores_gemma":[0.9977443,0.00003937349,0.001101417,0.0006117761,0.0004182372,0.00008491809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001464017,0.002533398,0.01343342,0.001937929,0.01700885,0.000003372337,0.01138302,0.007049278,0.1505142,0.7904693,0.004559375,0.0009614084],"study_design_scores_gemma":[0.0006634234,0.0002970398,0.004588625,0.0007343194,0.001767609,0.000008480704,0.001080023,0.05232437,0.9011002,0.03641073,0.0002748547,0.0007502815],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9565686,0.001711757,0.03401322,0.0002390439,0.0029191,0.001466228,0.0004028724,0.000191458,0.002487735],"genre_scores_gemma":[0.997728,0.00001914695,0.00199492,0.00001659483,0.0000600393,0.0001058357,0.00001305538,0.00001095181,0.00005141511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7540586,"threshold_uncertainty_score":0.9081522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488582966497776,"score_gpt":0.2428290984249875,"score_spread":0.2279432687600098,"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."}}