{"id":"W3215411791","doi":"10.1145/3478285","title":"Embedding Hierarchical Structures for Venue Category Representation","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Wilfrid Laurier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Embedding; Context (archaeology); Hierarchy; Representation (politics); Semantics (computer science); Information retrieval; Theoretical computer science; Artificial intelligence","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":[0.0003965094,0.0007051969,0.0006308817,0.003070021,0.0005837751,0.0007719918,0.001247456,0.001008934,0.002678924],"category_scores_gemma":[0.003028568,0.0003035505,0.0008339948,0.003753692,0.0005460789,0.002413186,0.001368842,0.001066749,0.001004475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007807628,"about_ca_system_score_gemma":0.0006725019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01226818,"about_ca_topic_score_gemma":0.0273934,"domain_scores_codex":[0.9994519,0.0001742491,0.0000333452,0.000181433,0.00009117416,0.00006784339],"domain_scores_gemma":[0.999086,0.0003277826,0.00009957869,0.0002600654,0.0001651378,0.00006139138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004602374,0.0003363686,0.01387918,0.000827454,0.0002573965,0.0003206026,0.001226601,0.1776881,0.01351786,0.08284324,0.02816446,0.6804785],"study_design_scores_gemma":[0.0000235291,0.00008623223,0.003106783,0.00006985157,0.00004648008,0.0001685792,0.0003680137,0.9084129,0.002324632,0.07441385,0.01093368,0.00004552947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07247712,0.00226307,0.9132767,0.0004164135,0.0001108511,0.0001649108,0.005280545,0.002387485,0.003622858],"genre_scores_gemma":[0.7272247,0.0009190767,0.257027,0.0001758629,0.0001013847,0.0003263259,0.01054664,0.0002045384,0.003474604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01226818,"threshold_uncertainty_score":0.02439356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02585947478299782,"score_gpt":0.2997060619374764,"score_spread":0.2738465871544786,"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."}}