{"id":"W2528943235","doi":"10.1007/978-3-319-62127-2_13","title":"A 2-Approximation for the Height of Maximal Outerplanar Graph Drawings","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Outerplanar graph; Embedding; Planar graph; Planar; Pathwidth; Book embedding; Graph drawing; Combinatorics; Graph; 1-planar graph; Mathematics; Computer science; Discrete mathematics; Chordal graph; Algorithm; Line graph; Computer graphics (images); 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":[],"consensus_categories":[],"category_scores_codex":[0.001054125,0.0003109708,0.0003535571,0.000557673,0.0005545114,0.0004941655,0.002933227,0.0001761224,0.000005679314],"category_scores_gemma":[0.0001313321,0.0002329378,0.0001825637,0.0002311697,0.0004928318,0.000616823,0.0005328725,0.000291507,0.000005339462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007812482,"about_ca_system_score_gemma":0.0003976078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001330978,"about_ca_topic_score_gemma":0.00003260675,"domain_scores_codex":[0.9976263,0.00001865385,0.0004498538,0.0008344455,0.0007326443,0.0003381131],"domain_scores_gemma":[0.9971052,0.0007291653,0.000528181,0.001163064,0.0004061122,0.00006828302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001448637,0.00002434816,0.00001454139,0.00006625907,0.00002481635,0.000005770522,0.0007260665,0.0566255,0.0002041408,0.1190822,0.00008391879,0.8231279],"study_design_scores_gemma":[0.0002446583,0.00013967,0.0001797449,0.0001423684,0.00001346542,0.00002749007,1.044478e-7,0.6515157,0.002067137,0.3421282,0.003261315,0.0002801863],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004536732,0.0004840977,0.994723,0.001594019,0.001637863,0.00062901,0.00001234782,0.00004528907,0.0008290564],"genre_scores_gemma":[0.2136572,0.00006865051,0.7833897,0.000992002,0.0009507368,0.00005158284,0.0000302409,0.00003198433,0.0008279065],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8228478,"threshold_uncertainty_score":0.9498926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040287601007561,"score_gpt":0.2566973345416801,"score_spread":0.2362944585316045,"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."}}