{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005590727,0.0003763984,0.0003828467,0.001302592,0.0002820136,0.0005530036,0.002540373,0.0004437009,0.0000438329],"category_scores_gemma":[0.0000841706,0.0003277887,0.00009515338,0.0006630177,0.0004750146,0.0006859484,0.001066709,0.0005347068,0.00008812355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001830726,"about_ca_system_score_gemma":0.0002404588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004756396,"about_ca_topic_score_gemma":0.000005683152,"domain_scores_codex":[0.9970196,0.00001836821,0.0004463705,0.001246947,0.0006600288,0.0006087256],"domain_scores_gemma":[0.9979123,0.0002255353,0.0002316519,0.00104828,0.000458765,0.0001234701],"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":[0.000001251456,0.00002178825,0.000008830825,0.00003346866,0.00000457746,0.000003808152,0.0001923041,0.007511524,0.000186349,0.04629283,0.00008418577,0.9456591],"study_design_scores_gemma":[0.0002262066,0.0001163578,0.000007235658,0.0001003064,0.000002036018,0.00001305994,7.129552e-8,0.7164662,0.0006346046,0.2791966,0.002941153,0.0002961899],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001575627,0.0001148423,0.990918,0.005034053,0.0009169679,0.001110793,0.000003734456,0.0002621664,0.001623649],"genre_scores_gemma":[0.002492297,0.00003458266,0.9946448,0.001517376,0.0002466092,0.00008602432,0.00001374654,0.00003036739,0.0009342429],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9453629,"threshold_uncertainty_score":0.9999174,"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."}}