Bibliographic record
Abstract
Web 2.0 and social media sites are becoming popular for both personal and professional use. These sites are high value targets for hackers that provide opportunities to access large quantities of personal or organizational information. The most recent security reports reveal that hacking attempts on Web 2.0 sites and social media are on the rise. In a special report entitled “Web 2.0 Hacking Incidents – 2009 Q1”, analysis of databases of successful hacking attempts in the first quarter of 2009 revealed that Web 2.0 sites are now the premier target for hackers. Statistics: • Web 2.0 sites make up 21 % of all reported hacking incidents • 95 % of user-generated comments on blogs, message boards, and chat rooms are either spam or contain malicious links • Over 60 % of the top 100 Web properties either hosted malicious content or redirected users to malicious sites without their knowledge • One new infected webpage is discovered every 3.6 seconds (four times faster than in first half of 2008)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".