{"id":"W2048384452","doi":"10.1145/2096149.2096155","title":"Modeling on quicksand","year":2012,"lang":"en","type":"article","venue":"ACM SIGCOMM Computer Communication Review","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Research England; Microsoft Research; National Science Foundation","keywords":"Computer science; Scalability; Robustness (evolution); Network topology; Scarcity; Distributed computing; Routing (electronic design automation); Sensitivity (control systems); Ground truth; Empirical research; Topology (electrical circuits); Machine learning; Computer network; Database","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":[0.002216546,0.0008055968,0.0008685134,0.00186353,0.00189542,0.004115616,0.003106088,0.002105971,0.04932909],"category_scores_gemma":[0.008817703,0.0007773934,0.001132486,0.00166403,0.002243992,0.007850212,0.003514674,0.002329094,0.00336996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004129732,"about_ca_system_score_gemma":0.004427247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02030531,"about_ca_topic_score_gemma":0.01385138,"domain_scores_codex":[0.9986688,0.0003447301,0.00006442253,0.0002821182,0.0003441674,0.0002957645],"domain_scores_gemma":[0.9968352,0.001254395,0.0002962684,0.0006105161,0.0006418241,0.0003619324],"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":[0.00004407024,0.00003398249,0.0006069215,0.00004945753,0.00001482941,0.0001157772,0.0001452169,0.2069642,0.0002883874,0.7803949,0.005014942,0.006327365],"study_design_scores_gemma":[0.00004856218,0.0000407383,0.0002567729,0.00005272693,0.00002330915,0.00009032535,0.0001565404,0.6262709,0.0004546453,0.3239192,0.04865221,0.00003420392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07560626,0.001767172,0.6654258,0.009835995,0.001042046,0.0004693455,0.004911045,0.002698057,0.2382444],"genre_scores_gemma":[0.7666003,0.002578739,0.1054535,0.001081347,0.0002939963,0.0006734707,0.002372476,0.0009381164,0.120008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04932909,"threshold_uncertainty_score":0.1650223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04288801640100692,"score_gpt":0.2883414826358308,"score_spread":0.2454534662348238,"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."}}