{"id":"W1558498035","doi":"10.4230/lipics.icalp.2016.74","title":"Approximating Directed Steiner Problems via Tree Embedding","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Simons Institute for the Theory of Computing, University of California Berkeley; McGill University; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Combinatorics; Steiner tree problem; Mathematics; Logarithm; Modulo; Approximation algorithm; Binary logarithm; Tree (set theory); Matching (statistics); Time complexity; Discrete mathematics; Hierarchy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003805397,0.0004666197,0.0004993101,0.0004269119,0.0003113666,0.0001995229,0.002888698,0.0003158768,0.00005456744],"category_scores_gemma":[0.00003603692,0.0005287528,0.0003280556,0.0009788072,0.0001549137,0.0005262204,0.003776555,0.0008827053,0.00007199738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001403214,"about_ca_system_score_gemma":0.0001100715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003137445,"about_ca_topic_score_gemma":0.00007263076,"domain_scores_codex":[0.997256,0.0001876842,0.0003560552,0.00148582,0.0001465432,0.0005678808],"domain_scores_gemma":[0.9974344,0.0001129596,0.0004317418,0.001622746,0.0001915285,0.0002066131],"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":[0.00004179538,0.0007235325,0.002282076,0.000800134,0.0006302641,0.0007409891,0.00428543,0.2300427,0.0003394013,0.718712,0.0005831362,0.0408186],"study_design_scores_gemma":[0.0002876921,0.00003817082,0.0002497758,0.000174945,0.00004226871,0.00000835409,0.00002845265,0.805417,0.0001399543,0.1928558,0.0002098756,0.0005476766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03615408,0.00008518254,0.946304,0.00003189945,0.0008590209,0.0004277841,0.000008153339,0.001228691,0.01490118],"genre_scores_gemma":[0.9255177,0.00004571656,0.07268017,0.00004885907,0.000117638,0.0000036166,0.00001797631,0.00003209019,0.001536199],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8893636,"threshold_uncertainty_score":0.9997164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09702573959776858,"score_gpt":0.1924085915130823,"score_spread":0.09538285191531376,"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."}}