{"id":"W4386688780","doi":"10.31237/osf.io/fvnh6","title":"Directional Graph Attention Network","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Graph; Attention network; Data mining; Margin (machine learning); Theoretical computer science; Artificial intelligence; Pattern recognition (psychology); 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":[0.0006435488,0.0009143213,0.0006175466,0.001429525,0.0004050471,0.0008453763,0.001145818,0.0009789206,0.003130921],"category_scores_gemma":[0.003222645,0.0003351188,0.000822299,0.001784064,0.0006665403,0.001797182,0.0009576324,0.0009980767,0.0006856198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001523731,"about_ca_system_score_gemma":0.000676505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01128294,"about_ca_topic_score_gemma":0.01131142,"domain_scores_codex":[0.9995617,0.0001122753,0.00001715527,0.000178064,0.00007969147,0.0000510452],"domain_scores_gemma":[0.9990553,0.0004792575,0.0001050385,0.00008539628,0.0002267649,0.0000482407],"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":[0.0002465227,0.0001247485,0.005678247,0.0004117062,0.0001962742,0.0002256632,0.00020307,0.5676678,0.006179249,0.09122112,0.01827189,0.3095737],"study_design_scores_gemma":[0.000007960273,0.00003388833,0.001025489,0.00001716313,0.00003938185,0.00006360313,0.00002133075,0.9503857,0.0008190605,0.04244903,0.005125684,0.00001171744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08004989,0.005205817,0.891336,0.002251489,0.0003859156,0.0001382641,0.001462541,0.001668082,0.01750201],"genre_scores_gemma":[0.8687302,0.004599855,0.104662,0.001089826,0.0003853619,0.0002196472,0.002714409,0.0002156812,0.01738311],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01128294,"threshold_uncertainty_score":0.02243453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03509300879736632,"score_gpt":0.2725107324210038,"score_spread":0.2374177236236374,"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."}}