News & Views: Cyberintrusion—Happening Much Closer to You Than You Might Think!
Bibliographic record
Abstract
According to the Internet Crime Complaint Center (IC3)2 (www.ic3.gov), cybercrimes are rising rapidly. In 2009, 336 655 cybercrime complaints were submitted to IC3, a 22.3% increase compared with 2008. Of these complaints, 146 663 were referred to law-enforcement agencies for further investigation. These referred cases represented a combined financial loss of $559.7 million. Although many may think these crimes were the results of careless individuals who fell for those “you just inherited a million dollars” scams, the reality is that if you have Internet connection, hackers have probably already attempted to attack the information stored in your computer. According to a report by Perkel (1), an average of 27 000 hacking attempts were made per day during the first quarter of 2010 at the San Diego Supercomputer Center of the University of California. The report also provides some practical tips to …
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 | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".