{"id":"W4283075104","doi":"10.48550/arxiv.2206.08164","title":"LRGB: Long Range Graph Benchmark","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Computer science; Benchmarking; Transformer; Graph; Theoretical computer science; Attention network; Artificial intelligence; Limiting; Machine learning","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":[0.001467522,0.002672836,0.0009408757,0.00232625,0.001442602,0.001326276,0.004533518,0.003146234,0.00520447],"category_scores_gemma":[0.009397503,0.0005035273,0.001739608,0.003315702,0.001144509,0.003040262,0.001413327,0.002751153,0.002697205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002635078,"about_ca_system_score_gemma":0.001993392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02779214,"about_ca_topic_score_gemma":0.05212628,"domain_scores_codex":[0.9983062,0.000470127,0.00008423753,0.0005570999,0.0003940229,0.0001883646],"domain_scores_gemma":[0.9963908,0.001667764,0.0002489056,0.0007743674,0.0006944769,0.0002236408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007571972,0.001238286,0.008016621,0.003147472,0.0005994531,0.0006115299,0.0002449239,0.2701643,0.004805477,0.01912891,0.5780404,0.1132455],"study_design_scores_gemma":[0.0006772842,0.0006763187,0.007552702,0.0002829391,0.000177717,0.0006465771,0.0005191232,0.792876,0.01162635,0.06450009,0.1203685,0.00009634646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4742057,0.01452154,0.09268909,0.01317193,0.00224791,0.001547641,0.2594095,0.06814715,0.07405961],"genre_scores_gemma":[0.4117917,0.002515105,0.1421133,0.002667424,0.0002865695,0.0009440097,0.4216663,0.003774059,0.01424156],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02779214,"threshold_uncertainty_score":0.05526072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05388534934177568,"score_gpt":0.1854765147608865,"score_spread":0.1315911654191108,"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."}}