{"id":"W2037688351","doi":"10.5555/545381.545430","title":"I/O-optimal algorithms for planar graphs using separators","year":2002,"lang":"en","type":"article","venue":"Symposium on Discrete Algorithms","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Planar graph; Planar straight-line graph; Breadth-first search; Planar; Algorithm; Book embedding; Computer science; Embedding; Outerplanar graph; Vertex (graph theory); Depth-first search; Graph; Search algorithm; Pathwidth; Theoretical computer science; Line graph; Artificial intelligence","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":[0.000489843,0.0007115955,0.0006434321,0.000572181,0.0008720577,0.0005272673,0.002073695,0.0002639313,0.00007877539],"category_scores_gemma":[0.00003497854,0.0006799398,0.0006069195,0.001225964,0.0002706365,0.0009456938,0.0002782557,0.0004423767,0.0001311025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001391653,"about_ca_system_score_gemma":0.00005494481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005542599,"about_ca_topic_score_gemma":0.000004093473,"domain_scores_codex":[0.9954542,0.0001453174,0.0007249918,0.001500494,0.0008711395,0.001303856],"domain_scores_gemma":[0.9972621,0.0003148271,0.0002813252,0.001489304,0.0001848118,0.0004676349],"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":[0.0003376103,0.003471734,0.0007006378,0.0005061171,0.001943485,0.0008148337,0.0103067,0.03862551,0.01456729,0.6589079,0.05640357,0.2134147],"study_design_scores_gemma":[0.001438731,0.0008766563,0.00006558306,0.00007732488,0.00005485813,0.000111871,0.00008044809,0.9636265,0.003574617,0.009473456,0.01950518,0.001114784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009900375,0.0007723764,0.9756552,0.002364377,0.003455321,0.001413042,0.0003357532,0.001002523,0.005101033],"genre_scores_gemma":[0.06366877,0.000335076,0.9302285,0.001864622,0.001294164,0.0003420258,0.00008623087,0.0001976692,0.001982957],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.925001,"threshold_uncertainty_score":0.9995652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04744852501361935,"score_gpt":0.2870264706846065,"score_spread":0.2395779456709872,"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."}}