Bibliometric Analysis and Visualization of Bayesian Network Application in Safety Field
Bibliographic record
Abstract
Bayesian network (BNs) has been widely used in uncertain knowledge modeling since it was proposed, especially in safety assessment and risk prediction. In order to fully understand the application status of Bayesian network in the field of safety, 3111 sample documents were collected from the web of science core collection database. Through CiteSpace technology text mining and visual analysis software, document output, keywords, author cooperation, organization cooperation, citation and journal distribution were analyzed. The results show that: FAISAL KHAN, MOHAMED ABDELATY, HELAI HUANG, TAREK SAYED, JAEYOUNG LEE, etc. published Bayesian networks The most widely used literature in the field of safety has made an important contribution to the development of this field; institutional cooperation is mainly based on Univ Central Florida, MEM Univ Newfoundland, Univ British Columbia, Tongji Univ, Texas A & M Univ, cents Univ, Delft Univ technology and other universities; journal distribution of literature is mainly based on the core functions of Bayesian network, and mainly distributed in ACCIDENT ANALYSIS AND PREVENTION, TRANSPORTATION RESEARCH RECODE, RELIABILITY ENGINEERING SYSTEM SAFETY and SAFETY SCIENCE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.077 | 0.088 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".