{"id":"W2963480423","doi":"10.1007/s40305-019-00260-1","title":"Approximation Algorithms for Vertex Happiness","year":2019,"lang":"en","type":"article","venue":"Journal of the Operations Research Society of China","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Shandong Province; China Scholarship Council; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Combinatorics; Hypergraph; Vertex (graph theory); Approximation algorithm; Inverse; Upper and lower bounds; Mathematics; Linear programming relaxation; Physics; Discrete mathematics; Graph; Algorithm; Linear programming; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002596198,0.00007089652,0.0001748981,0.00008613673,0.0004247456,0.0001762178,0.001482557,0.00005020203,0.00001699332],"category_scores_gemma":[0.0001769186,0.00004615684,0.0003778102,0.0006796542,0.0001354637,0.000682252,0.0002673105,0.0003802797,0.000003572698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006223299,"about_ca_system_score_gemma":0.0002992616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002184439,"about_ca_topic_score_gemma":0.00000298664,"domain_scores_codex":[0.9983616,0.0001604509,0.0003495664,0.0001247253,0.0008058182,0.000197843],"domain_scores_gemma":[0.9982214,0.0002124293,0.000104708,0.0004260297,0.0009836541,0.00005179668],"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":[0.00009186992,0.001719516,0.0007890792,0.0008311611,0.0007706267,0.000001448683,0.02850734,0.08806143,0.06800262,0.5606829,0.0993642,0.1511779],"study_design_scores_gemma":[0.000671313,0.0002316437,0.00270095,0.00009848281,0.000005221534,0.00002083143,0.0002699094,0.958759,0.004465964,0.02945939,0.003242755,0.00007454339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08253057,0.0002493646,0.8979488,0.01719564,0.0008892491,0.0007138354,0.0000102486,0.00001147003,0.0004508194],"genre_scores_gemma":[0.5361577,0.0001282481,0.4616482,0.0001380862,0.0002266089,0.0000171519,0.00000202619,0.00001082772,0.001671093],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8706976,"threshold_uncertainty_score":0.3266843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06820800589132216,"score_gpt":0.3611336697797232,"score_spread":0.2929256638884011,"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."}}