{"id":"W2187039192","doi":"10.1007/978-3-642-40450-4_54","title":"Better Approximation Algorithms for Technology Diffusion","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Rounding; Cascade; Algorithm; Computer science; Vertex (graph theory); Graph; Upgrade; Approximation algorithm; Combinatorics; Binary logarithm; Discrete mathematics; Mathematics; Theoretical computer science","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":[0.003590233,0.001858112,0.002559192,0.001928619,0.0009157142,0.003374787,0.003363281,0.002962749,0.01444597],"category_scores_gemma":[0.01768774,0.0008741706,0.002132725,0.004249935,0.001750066,0.008464339,0.002331068,0.006518768,0.00246117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003220077,"about_ca_system_score_gemma":0.001766055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004208432,"about_ca_topic_score_gemma":0.003650283,"domain_scores_codex":[0.9979193,0.0008368856,0.0001166918,0.0003171622,0.0005866067,0.0002233772],"domain_scores_gemma":[0.9942075,0.003488728,0.0002796968,0.001272454,0.0005568566,0.0001947705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001838972,0.0001423666,0.000425024,0.0002678257,0.00009512608,0.00004421239,0.0001496335,0.1781276,0.0008596864,0.6288099,0.0271462,0.1637485],"study_design_scores_gemma":[0.00005841843,0.00002577986,0.0001422651,0.00004412512,0.00003540724,0.00005245877,0.00003197792,0.5026562,0.0003689803,0.4860707,0.01049926,0.00001446424],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006686496,0.003830947,0.9717747,0.002058086,0.0005892236,0.00005922735,0.0002523547,0.0007047451,0.01404435],"genre_scores_gemma":[0.1969434,0.006667522,0.7493474,0.001155988,0.001160864,0.000362865,0.001039098,0.0009572309,0.04236567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01444597,"threshold_uncertainty_score":0.04832655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02051953214541975,"score_gpt":0.2565089051580965,"score_spread":0.2359893730126768,"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."}}