{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006832321,0.001831168,0.001144161,0.003478403,0.0008451729,0.001431593,0.001875601,0.001437742,0.004896095],"category_scores_gemma":[0.004098308,0.0005550126,0.001334096,0.00331686,0.000411359,0.002794305,0.001933184,0.001522284,0.00203329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008230202,"about_ca_system_score_gemma":0.00116905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01626181,"about_ca_topic_score_gemma":0.02865458,"domain_scores_codex":[0.9995399,0.00008577217,0.00002498013,0.000171323,0.0001226131,0.00005543622],"domain_scores_gemma":[0.9990776,0.0003225114,0.00007729082,0.0002652016,0.0001930695,0.0000643723],"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":[0.0008253106,0.0006416122,0.01033639,0.0007282199,0.0005939328,0.0004659878,0.0001895123,0.1233175,0.01144957,0.01267072,0.1041116,0.7346696],"study_design_scores_gemma":[0.00005152366,0.0001113114,0.001118115,0.00003769142,0.00006938796,0.0001796046,0.00007392829,0.9680389,0.003976031,0.01472604,0.01158659,0.00003095941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08409999,0.002172065,0.8318862,0.0009465578,0.0007745442,0.0006457968,0.02682515,0.04757291,0.005076856],"genre_scores_gemma":[0.3803761,0.001291505,0.5438893,0.0004762465,0.0002254443,0.0005887413,0.06368989,0.001731566,0.007731076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01626181,"threshold_uncertainty_score":0.03233433,"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."}}