{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001087755,0.0002179594,0.0003608429,0.00006558255,0.0002576401,0.0001094382,0.003695946,0.00005935133,0.00003527823],"category_scores_gemma":[0.0000735435,0.0001919969,0.0001427391,0.0003668893,0.00002981979,0.0006172464,0.001099873,0.0003304648,0.0004636928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004313238,"about_ca_system_score_gemma":0.00003159037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006769035,"about_ca_topic_score_gemma":0.000001451763,"domain_scores_codex":[0.9980264,0.0005785454,0.0004801639,0.0002605441,0.0002827275,0.0003716286],"domain_scores_gemma":[0.9949245,0.000500189,0.0001431446,0.004117628,0.0001252842,0.0001892557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002012732,0.0001338232,0.00008363144,0.00009405605,0.00002471178,4.20471e-7,0.0001344499,0.00187034,8.584715e-7,0.08774994,0.01387434,0.8960314],"study_design_scores_gemma":[0.0003854643,0.00005614648,0.0001208565,0.001862228,0.00003028267,0.00002298986,0.000002797516,0.8095465,0.00000291181,0.0009184749,0.1866944,0.0003570096],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004311236,0.1623022,0.815805,0.01824111,0.0004075898,0.0004560162,6.12412e-7,0.0004049885,0.001951358],"genre_scores_gemma":[0.7354602,0.06585856,0.1694138,0.02869176,0.0003279514,0.0001280827,0.00002071994,0.00002339064,0.00007555803],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8956744,"threshold_uncertainty_score":0.7829406,"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."}}