{"id":"W256842657","doi":"10.1613/jair.4030","title":"Inapproximability of Treewidth and Related Problems","year":2014,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Treewidth; Partial k-tree; Pathwidth; Combinatorics; Tree-depth; Graphical model; Tree decomposition; Mathematics; Chordal graph; Discrete mathematics; Computer science; Graph; 1-planar graph; Line graph; Artificial intelligence","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":[0.004738964,0.002144797,0.003024451,0.001800839,0.001776067,0.005373297,0.005851966,0.003536266,0.00929116],"category_scores_gemma":[0.0429528,0.001798549,0.003601935,0.003726824,0.003947707,0.01356358,0.003090178,0.009104516,0.001154159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003988109,"about_ca_system_score_gemma":0.002340607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003890474,"about_ca_topic_score_gemma":0.003723189,"domain_scores_codex":[0.994436,0.001795525,0.0002272627,0.001448599,0.001250053,0.0008425108],"domain_scores_gemma":[0.932134,0.06035396,0.002331807,0.002915544,0.001236168,0.00102838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008520202,0.000777191,0.004007733,0.001369882,0.0003317998,0.0003994243,0.00132631,0.4502202,0.004709152,0.4324094,0.02621994,0.077377],"study_design_scores_gemma":[0.0000864168,0.00005189864,0.0003397724,0.0000655293,0.00005835221,0.00016572,0.0001419949,0.3432363,0.00101658,0.6524714,0.002341804,0.00002416666],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1783924,0.005043356,0.7775353,0.01254744,0.0003053859,0.0002903121,0.003107969,0.001870189,0.02090758],"genre_scores_gemma":[0.6878986,0.00537701,0.2867139,0.002738751,0.001082563,0.0006975748,0.003985377,0.001168921,0.01033732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00929116,"threshold_uncertainty_score":0.03108209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.142345972613428,"score_gpt":0.382805798384195,"score_spread":0.240459825770767,"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."}}