{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002945163,0.002260085,0.003066936,0.003365096,0.002754142,0.006074242,0.006092588,0.002879926,0.01820201],"category_scores_gemma":[0.01713926,0.001239553,0.003601623,0.006396853,0.0023258,0.01081054,0.0056543,0.006616477,0.002811389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005143045,"about_ca_system_score_gemma":0.004326976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004991183,"about_ca_topic_score_gemma":0.006221277,"domain_scores_codex":[0.9972067,0.0007594755,0.0001285227,0.0007217766,0.0005619768,0.0006215767],"domain_scores_gemma":[0.9917924,0.004418009,0.0004839331,0.00193952,0.0006630238,0.0007031368],"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":[0.001330476,0.0007260527,0.003810978,0.0005327876,0.0003014287,0.0001430421,0.0008155916,0.1901194,0.001968338,0.4704228,0.04982009,0.280009],"study_design_scores_gemma":[0.0001238554,0.00008448977,0.0004596138,0.00006747208,0.0001302703,0.0001126807,0.0002654005,0.5429348,0.001290093,0.447697,0.006807658,0.00002666297],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1106127,0.00300733,0.834785,0.006731427,0.000580708,0.0005314012,0.002626842,0.002648627,0.03847593],"genre_scores_gemma":[0.5715739,0.002218866,0.3867393,0.001111243,0.0006723098,0.0007553006,0.005069796,0.001116534,0.03074278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01820201,"threshold_uncertainty_score":0.06089181,"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."}}