{"id":"W2001622826","doi":"10.1109/ciss.2013.6552253","title":"Active learning of multiple source multiple destination topologies","year":2013,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Merge (version control); Subnetwork; Computer science; Network topology; Topology (electrical circuits); Upper and lower bounds; Focus (optics); Greedy algorithm; Algorithm; Theoretical computer science; Combinatorics; Mathematics; Computer network; Physics","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":[0.0001291988,0.00008563241,0.0001110425,0.00007338624,0.000111186,0.0000591145,0.0003509776,0.00003999046,0.00007529182],"category_scores_gemma":[0.0008517629,0.00006915494,0.00003974032,0.0001736955,0.00004280498,0.0003755327,0.0001858625,0.0001690824,0.00009009671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000135452,"about_ca_system_score_gemma":0.00001492923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001425767,"about_ca_topic_score_gemma":0.000009791773,"domain_scores_codex":[0.9992471,0.0000913376,0.0001385053,0.0002040151,0.0001478162,0.0001712253],"domain_scores_gemma":[0.999068,0.0004831308,0.0001043906,0.0001886316,0.0001160926,0.00003979259],"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":[0.000003156288,0.00006592304,0.07660615,0.00001416003,0.00001383875,8.73978e-7,0.001929987,0.0163716,0.005867869,0.003290321,0.0002545519,0.8955816],"study_design_scores_gemma":[0.0002543494,0.00008585471,0.05549571,0.000007534885,0.00000123081,0.000003205834,0.0003645997,0.9295191,0.01270978,0.0004470762,0.001011372,0.0001002424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2544058,0.00001118842,0.7416565,0.0004250564,0.00007621588,0.00009245293,1.338912e-7,0.0002801274,0.003052493],"genre_scores_gemma":[0.8907614,0.000001852629,0.1063721,0.00003831012,0.00002420239,0.00001201444,0.000001533076,0.000004735454,0.002783885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9131474,"threshold_uncertainty_score":0.2820057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118886000462138,"score_gpt":0.2357621557532298,"score_spread":0.2245732957486084,"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."}}