{"id":"W2170069407","doi":"10.1145/1187436.1210588","title":"Partitioning planar graphs with costs and weights","year":2007,"lang":"en","type":"article","venue":"ACM Journal of Experimental Algorithmics","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Graph partition; Book embedding; Planar graph; Combinatorics; Planar straight-line graph; Bounded function; Partition (number theory); Computer science; Graph; Outerplanar graph; Mathematics; Discrete mathematics; Pathwidth; 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":[0.0007499533,0.001141118,0.0008074178,0.00180651,0.0008009521,0.001594081,0.001358396,0.0009115352,0.00262699],"category_scores_gemma":[0.01001373,0.0007508654,0.000719329,0.002689016,0.0009460033,0.003372405,0.00218714,0.001084507,0.0005552152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195375,"about_ca_system_score_gemma":0.001100592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003392149,"about_ca_topic_score_gemma":0.004575341,"domain_scores_codex":[0.9985837,0.0002236553,0.00009501744,0.0002801355,0.0006570637,0.0001604647],"domain_scores_gemma":[0.9946055,0.002529559,0.0007757264,0.001082912,0.0008781769,0.0001280904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006162442,0.0001590902,0.004475978,0.0004361428,0.000113395,0.0002387842,0.000299588,0.6185828,0.0571155,0.07626744,0.003749208,0.2379458],"study_design_scores_gemma":[0.000103653,0.0001710482,0.002495355,0.00003603345,0.0000920356,0.0003296357,0.000331129,0.8137435,0.05024408,0.12457,0.007801059,0.00008243176],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2100763,0.0001999593,0.782519,0.0002939074,0.00003945243,0.0002077975,0.0004512926,0.00154186,0.004670375],"genre_scores_gemma":[0.401096,0.0003472111,0.5937042,0.00009072888,0.0000265938,0.0002321486,0.001337104,0.000449445,0.002716628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003392149,"threshold_uncertainty_score":0.008788168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649643846463986,"score_gpt":0.2993828352694884,"score_spread":0.2828863968048486,"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."}}