{"id":"W4388699524","doi":"10.3390/app132212338","title":"Event Knowledge Graph: A Review Based on Scientometric Analysis","year":2023,"lang":"en","type":"review","venue":"Applied Sciences","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Beijing Association for Science and Technology; Ministry of Natural Resources of the People's Republic of China; China Scholarship Council; Strong","keywords":"Data science; Computer science; Citation; Field (mathematics); Event (particle physics); Knowledge graph; Graph; Network analysis; Power graph analysis; Information retrieval; World Wide Web; Engineering","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":["metaepi_narrow","bibliometrics","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003721747,0.0007503721,0.002571675,0.01004412,0.0006804168,0.0003967449,0.006460077,0.0001943601,0.00002345631],"category_scores_gemma":[0.0001924899,0.0005263025,0.001587596,0.1694883,0.0005427749,0.0002507808,0.0007683859,0.0005778926,0.0008193259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001402973,"about_ca_system_score_gemma":0.0005813845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002734089,"about_ca_topic_score_gemma":0.000009642504,"domain_scores_codex":[0.993403,0.0003028082,0.001108731,0.002429073,0.001717351,0.001039026],"domain_scores_gemma":[0.9950231,0.00181486,0.0008728885,0.001862432,0.00009916068,0.0003275007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[3.130893e-7,0.00006797705,0.000001101771,0.005842801,0.0001231888,0.000008961293,0.000008091338,0.001197115,3.716797e-8,0.01539458,0.001928734,0.9754271],"study_design_scores_gemma":[0.0001051122,0.0001395969,0.000008100581,0.02113954,0.001816291,0.000004602096,0.000002484759,0.01775281,6.687195e-7,0.002021712,0.9558408,0.001168327],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[8.349726e-8,0.9333819,0.05939665,0.00008588192,0.0005639374,0.001304434,0.00001000419,0.0004799735,0.004777088],"genre_scores_gemma":[0.00002524193,0.9934928,0.005067104,0.0005489837,0.00007831551,0.0005321979,0.00002153122,0.00003063053,0.0002031887],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9742588,"threshold_uncertainty_score":0.9999586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1278573235042788,"score_gpt":0.4173282751491699,"score_spread":0.2894709516448911,"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."}}