{"id":"W3080519191","doi":"10.1007/978-3-030-57796-4_22","title":"Triangle Enumeration on Massive Graphs Using AWS Lambda Functions","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Enumeration; Lambda; Computer science; Concurrency; Combinatorics; Graph; Task (project management); Theoretical computer science; Parallel computing; Algorithm; Discrete mathematics; Mathematics; Distributed computing; Engineering","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":[0.0003276686,0.0003644047,0.0005220494,0.0003229151,0.0002795604,0.0002232272,0.0003680226,0.0001611389,0.000006213007],"category_scores_gemma":[0.00002146066,0.0003480293,0.0001446783,0.0001697207,0.00006450789,0.0003437708,0.0001728007,0.0004387698,0.00003189282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007419727,"about_ca_system_score_gemma":0.00003506101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000156915,"about_ca_topic_score_gemma":0.000008080188,"domain_scores_codex":[0.9980004,0.00009053466,0.0006530752,0.0007238635,0.0002778048,0.0002542907],"domain_scores_gemma":[0.9987647,0.0002800992,0.0004442375,0.0003372643,0.00007294357,0.0001007892],"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":[0.0000102666,0.00001452842,0.00001865429,0.00009209191,0.00003280026,0.00003994348,0.0004115994,0.02686763,0.00001049473,0.9117891,0.00002242682,0.06069047],"study_design_scores_gemma":[0.0004782256,0.0005223582,0.000006204157,0.003316858,0.00004796949,0.00008480148,0.0004995468,0.6774293,0.000133509,0.184946,0.1313487,0.001186498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002204725,0.010704,0.9362267,0.00006486157,0.004245096,0.0005508618,0.00001318699,0.0001407426,0.04783405],"genre_scores_gemma":[0.9541163,0.002465925,0.01219027,0.0006244882,0.002024761,0.00004381887,0.00007289158,0.0001660284,0.0282955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9538959,"threshold_uncertainty_score":0.9998972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02813843027583655,"score_gpt":0.2608050280559165,"score_spread":0.2326665977800799,"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."}}