{"id":"W4285404855","doi":"10.1016/j.ins.2022.07.031","title":"GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings","year":2022,"lang":"en","type":"article","venue":"Information Sciences","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; Wuhan University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Key Research and Development Program of China; York University","keywords":"Computer science; Embedding; Knowledge graph; Graph; Relation (database); Task (project management); Coronavirus disease 2019 (COVID-19); Artificial intelligence; Feature (linguistics); Theoretical computer science; Machine learning; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.00195187,0.0001414175,0.00014484,0.0005499833,0.00299958,0.0005070676,0.001231529,0.00005954635,0.00003350858],"category_scores_gemma":[0.00014192,0.0001349334,0.00009379742,0.002988895,0.000111437,0.002558386,0.0003635602,0.0003533236,0.00001210219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001261472,"about_ca_system_score_gemma":0.0002481839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001450032,"about_ca_topic_score_gemma":0.00004595724,"domain_scores_codex":[0.9982447,0.0001388002,0.0003593698,0.0002791279,0.0005642483,0.0004137396],"domain_scores_gemma":[0.998829,0.000279678,0.0003330891,0.0002590625,0.0002060223,0.00009313057],"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":[0.00006145801,0.00007150575,0.01245483,0.0001531258,0.0000235788,0.000001357188,0.02758105,0.3476666,0.00002769836,0.2033241,0.06397919,0.3446555],"study_design_scores_gemma":[0.000252267,0.0002949495,0.003655694,0.00001198872,0.000002737681,0.00003391911,0.001137792,0.8654714,0.00002045152,0.002707178,0.1262217,0.0001899615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01420017,0.0006774419,0.9616974,0.003753516,0.004278741,0.001077321,0.00001882592,0.0006268278,0.01366973],"genre_scores_gemma":[0.9790456,0.00002210425,0.01875115,0.001478525,0.00008274979,0.0002987815,0.00002615972,0.000006161121,0.0002887949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9648454,"threshold_uncertainty_score":0.9982984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04315029785271868,"score_gpt":0.3422635033324763,"score_spread":0.2991132054797576,"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."}}