{"id":"W2443199731","doi":"","title":"Inapproximability of treewidth and related problems","year":2015,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Treewidth; Partial k-tree; Pathwidth; Tree-depth; Graphical model; Tree decomposition; Combinatorics; Computer science; Mathematics; 1-planar graph; Inference; Discrete mathematics; Graph; Chordal graph; Theoretical computer science; Artificial intelligence; Line graph","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":[0.004597373,0.00254304,0.003260809,0.001947403,0.00211516,0.007133087,0.005899872,0.003915017,0.01050683],"category_scores_gemma":[0.04149881,0.00191618,0.004220172,0.004044127,0.004291477,0.01480163,0.003570919,0.009728071,0.001513861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004186301,"about_ca_system_score_gemma":0.002428965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003605273,"about_ca_topic_score_gemma":0.003120507,"domain_scores_codex":[0.9934123,0.001894373,0.0002884182,0.001760987,0.001499636,0.001144345],"domain_scores_gemma":[0.9351272,0.05619656,0.002633583,0.003556244,0.001463857,0.001022463],"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":[0.00123221,0.0008890684,0.005417077,0.002090233,0.0004689708,0.0005467692,0.001729934,0.4084981,0.00665605,0.4576705,0.03554016,0.07926092],"study_design_scores_gemma":[0.0001036629,0.00007010764,0.0004374772,0.00009557115,0.00007501015,0.0002698409,0.0002089966,0.2573353,0.001345963,0.7366906,0.003338052,0.00002931361],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2421096,0.008616674,0.6868476,0.0180939,0.0004943081,0.000377275,0.004828931,0.002451061,0.03618066],"genre_scores_gemma":[0.7149357,0.0066467,0.2537917,0.003475225,0.001383293,0.0007479977,0.005053284,0.00139663,0.0125694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01050683,"threshold_uncertainty_score":0.03514886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1818545937339058,"score_gpt":0.3737240228359098,"score_spread":0.191869429102004,"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."}}