{"id":"W3119774137","doi":"","title":"Cyber security trend analysis using web of science: A bibliometric analysis","year":2020,"lang":"en","type":"article","venue":"","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Field (mathematics); Trend analysis; Cloud computing; Web of science; Directory; Computer science; Computer security; Data science; Political science; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003308447,0.00044917,0.0010145,0.1348774,0.001325289,0.005234872,0.0005926642,0.0007436682,0.005000875],"category_scores_gemma":[0.0186479,0.0002436709,0.001381182,0.1643382,0.0005333562,0.00389257,0.00150335,0.0006500689,0.001725987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001966368,"about_ca_system_score_gemma":0.003502152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007363113,"about_ca_topic_score_gemma":0.006624822,"domain_scores_codex":[0.9936997,0.0007358126,0.001065319,0.0004534343,0.00375046,0.0002952647],"domain_scores_gemma":[0.9800752,0.01061743,0.003487264,0.0007086408,0.004622929,0.000488591],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002002488,0.0004552071,0.5609644,0.006212478,0.000892807,0.001104026,0.004115683,0.003228515,0.00267041,0.01292324,0.03031107,0.3769219],"study_design_scores_gemma":[0.00003950691,0.0002884631,0.8235496,0.001382089,0.0007211839,0.001871153,0.009779566,0.01861165,0.003389244,0.005372307,0.1348314,0.0001637913],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8181771,0.01395324,0.009667323,0.002712662,0.0002593996,0.001000401,0.09247777,0.001105302,0.06064674],"genre_scores_gemma":[0.9277132,0.01104197,0.01462432,0.0001274741,0.0003510688,0.0008250365,0.03908871,0.0001528913,0.006075321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9966915,"threshold_uncertainty_score":0.01749694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04388665236434727,"score_gpt":0.3109506231084496,"score_spread":0.2670639707441024,"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."}}