{"id":"W4384613985","doi":"10.48550/arxiv.2307.07107","title":"Graph Positional and Structural Encoder","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Encoder; Graph; Identifiability; Artificial intelligence; Theoretical computer science; Machine learning; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000765677,0.0002824637,0.0002337351,0.0002766229,0.0002001309,0.0001122273,0.001108931,0.0002185163,0.000009402052],"category_scores_gemma":[0.00001044082,0.0003173391,0.0001456905,0.0006344355,0.0001671284,0.0004384129,0.002294573,0.0005914292,0.00002770905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005664008,"about_ca_system_score_gemma":0.00005195595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003693226,"about_ca_topic_score_gemma":0.00003157282,"domain_scores_codex":[0.9982412,0.00007409385,0.0001410414,0.001106032,0.00009798243,0.0003395802],"domain_scores_gemma":[0.9987425,0.0001277332,0.0001426371,0.0007299517,0.00008963279,0.0001675323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002019303,0.00001305216,0.005633936,0.00004204744,0.00009351556,0.0006703446,0.0001157291,0.500824,0.00003638552,0.4912886,0.0005599399,0.0007021988],"study_design_scores_gemma":[0.0001998785,0.000023329,0.01510383,0.00003994187,0.00002051187,0.00001600096,0.00001077362,0.4216691,0.00001622769,0.5625293,0.00003894898,0.0003321917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3079426,0.00009885279,0.689654,0.0002659365,0.0009814583,0.0002096206,0.00002729351,0.0005417599,0.0002784676],"genre_scores_gemma":[0.9939626,0.0001771681,0.004845049,0.0001412266,0.00008230997,7.671437e-7,0.0000246833,0.00001869761,0.0007474458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6860201,"threshold_uncertainty_score":0.9999279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06640210577149619,"score_gpt":0.1932313591957524,"score_spread":0.1268292534242562,"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."}}