{"id":"W1523269886","doi":"10.48550/arxiv.1411.7277","title":"$1$-String $B_2$-VPG Representation of Planar Graphs","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Planar; Combinatorics; Planar graph; String (physics); Representation (politics); Enhanced Data Rates for GSM Evolution; Graph; Mathematics; Grid; Computer science; Geometry; Artificial intelligence; Computer graphics (images)","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":[0.0002201314,0.000182669,0.00026185,0.0004357312,0.00009512791,0.00005959047,0.00090459,0.0001599892,0.00001165892],"category_scores_gemma":[0.00004728909,0.0002242723,0.0001804596,0.0008350534,0.00004989965,0.0002876951,0.0005429228,0.0002391839,0.0000220894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005945872,"about_ca_system_score_gemma":0.0001145931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009127837,"about_ca_topic_score_gemma":0.00001396924,"domain_scores_codex":[0.9985881,0.0001411183,0.0002372894,0.0007263921,0.0001316116,0.0001755],"domain_scores_gemma":[0.9984409,0.0001505331,0.0003444543,0.0007627761,0.0002114408,0.00008988247],"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":[0.0000119723,0.00003431415,0.001327573,0.00004553676,0.00004325437,0.00001883769,0.00008794485,0.4991471,0.0001718869,0.4973671,0.000316892,0.001427677],"study_design_scores_gemma":[0.0004212992,0.00005812219,0.004917802,0.00007333756,0.00004859781,0.000004721025,0.00002365299,0.6872594,0.002459825,0.3040123,0.0003931034,0.0003277582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3220368,0.00002878749,0.6763324,0.00005344671,0.0004562534,0.000126951,0.000006092055,0.00008263219,0.0008766557],"genre_scores_gemma":[0.992676,0.00006156878,0.006590895,0.00005135504,0.00007271732,6.473539e-7,0.00005848927,0.000007629058,0.0004806608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6706392,"threshold_uncertainty_score":0.914556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07463916650787789,"score_gpt":0.2014598193653725,"score_spread":0.1268206528574946,"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."}}