Bibliographic record
Abstract
With the wide application of Internet technology, the application scope of big data technology in all walks of life is expanding. The widespread use of the Internet has led to the generation and accumulation of massive data, which is not only a valuable information resource, but also a challenge to information security. In the current big data environment, computer information security faces many key technologies and challenges, such as data privacy protection, network attack prevention, identity authentication, and so on. To address these challenges, this article will focus on exploring the application of big data technology in the field of computer information security. In the big data environment, data storage, transmission, and processing face more complex security issues. At the same time, the application of big data technology also provides new possibilities for information security, such as security event detection and response systems based on big data analysis and network intrusion detection systems based on behavior analysis. Through in-depth analysis and effective protection strategies in this article, the aim is to provide practical references for related research and promote the continuous development and innovation in the field of computer information security. In the research, we will further explore the integration of big data technology with emerging technologies such as artificial intelligence and blockchain to address the increasingly complex challenges in the field of information security and achieve a positive interaction between information security and technological innovation.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".