{"id":"W2257178227","doi":"10.1007/s00454-017-9939-y","title":"Embedding-Preserving Rectangle Visibility Representations of Nonplanar Graphs","year":2017,"lang":"en","type":"article","venue":"Discrete & Computational Geometry","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Ministero dell’Istruzione, dell’Università e della Ricerca; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Rectangle; Embedding; Time complexity; Book embedding; Graph; Visibility; Crossing number (knot theory); Set (abstract data type)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006775901,0.0002193914,0.0003059521,0.0004906391,0.0008839577,0.0004395234,0.001656111,0.00008701275,0.00009516683],"category_scores_gemma":[0.0007125351,0.0002290246,0.0002153362,0.0007221454,0.0001649184,0.001470272,0.0006755913,0.0001808148,0.00003421282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004847555,"about_ca_system_score_gemma":0.0001622491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001334395,"about_ca_topic_score_gemma":0.00001574504,"domain_scores_codex":[0.9974719,0.0001227882,0.0005962412,0.0006838756,0.0008184013,0.0003067548],"domain_scores_gemma":[0.9969298,0.0005145102,0.0005829391,0.001258299,0.0005586834,0.0001557443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006010156,0.0003383611,0.04249161,0.0001274929,0.0002258038,0.00002747014,0.0008373149,0.6772526,0.001719281,0.2513881,0.003376367,0.02215544],"study_design_scores_gemma":[0.000597565,0.00009804219,0.3374646,0.00004850652,0.0000191824,0.0000211437,0.00005642949,0.509225,0.001332033,0.1503952,0.0004164259,0.000326003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3212557,0.0001384117,0.6723431,0.0008326506,0.0005407705,0.0002305273,0.00005124495,0.0001041255,0.004503463],"genre_scores_gemma":[0.8963027,0.000008303245,0.1030803,0.0001136038,0.00009645659,0.00001765607,0.0001362065,0.00001242841,0.0002323727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.575047,"threshold_uncertainty_score":0.9339354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02211987090027542,"score_gpt":0.3263541091423416,"score_spread":0.3042342382420662,"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."}}