{"id":"W2907079035","doi":"10.48550/arxiv.1901.01484","title":"LanczosNet: Multi-Scale Deep Graph Convolutional Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Computer science; Graph; Scale (ratio); Artificial intelligence; Theoretical computer science; Cartography; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001785895,0.0005323555,0.0005222161,0.0003095172,0.0002143966,0.0001168342,0.002831206,0.0005810281,0.00003166224],"category_scores_gemma":[0.00001249066,0.0006237194,0.0004349037,0.0009729741,0.0002265372,0.0005433225,0.002804748,0.001344493,0.0001661161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001818087,"about_ca_system_score_gemma":0.0001131195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004539332,"about_ca_topic_score_gemma":0.0001115856,"domain_scores_codex":[0.9968526,0.000180075,0.000293324,0.001777935,0.0001450782,0.0007509367],"domain_scores_gemma":[0.9970763,0.0001901841,0.0003775552,0.001867003,0.0002090086,0.0002799756],"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":[0.00002247654,0.00007592396,0.009610408,0.00002432225,0.00006972579,0.0001369567,0.00004188165,0.9468793,0.00000824954,0.04227466,0.0004303335,0.0004257835],"study_design_scores_gemma":[0.0007217391,0.00003871593,0.005876459,0.0000697651,0.00003896664,0.000009982529,0.00001445289,0.9685754,0.00001062229,0.02349776,0.0004737284,0.0006724026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01912174,0.0005135802,0.9762119,0.00006568091,0.002265628,0.0004787651,0.00001583405,0.0005017431,0.0008251299],"genre_scores_gemma":[0.9809318,0.0004623001,0.01640219,0.0003222918,0.0001940815,0.000002411216,0.00005737918,0.0000381911,0.001589406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.96181,"threshold_uncertainty_score":0.9996214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05126511466307922,"score_gpt":0.1852913280843273,"score_spread":0.1340262134212481,"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."}}