{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001113917,0.0001102209,0.0001359267,0.0001587672,0.000296652,0.0002713798,0.0003788969,0.00008218143,0.000003789634],"category_scores_gemma":[0.00006919887,0.0001112701,0.00009261555,0.0004554683,0.00002121389,0.00199621,0.00001106833,0.0001792005,0.00002232934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005132575,"about_ca_system_score_gemma":0.00005452289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008102415,"about_ca_topic_score_gemma":0.000001675492,"domain_scores_codex":[0.9989278,0.00006495661,0.0003769806,0.0001818571,0.0002566683,0.0001917493],"domain_scores_gemma":[0.9986632,0.0002761649,0.0001399943,0.000652031,0.0002003774,0.00006819061],"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":[0.00003276771,0.00002380559,0.00001690067,0.00009958773,0.00004566948,0.00000384159,0.002103294,0.7771144,0.0003565975,0.0449947,0.000652509,0.174556],"study_design_scores_gemma":[0.001974661,0.0002000293,0.0007242594,0.0001169523,0.00003462238,0.0003032249,0.00151718,0.8601878,0.01743818,0.04274246,0.07407431,0.0006863737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001517072,0.00005096545,0.9957322,0.0004348177,0.001572244,0.000316514,0.00002820444,0.0001695557,0.0001784063],"genre_scores_gemma":[0.9514136,0.00001973656,0.04781723,0.0003059565,0.00008580589,0.000191074,0.00006559638,0.000008081837,0.00009296741],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9498965,"threshold_uncertainty_score":0.4537463,"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."}}