Bibliographic record
Abstract
Imagine you are sitting at a public coffee shop and using their free wifi on your work device. As long as you are connected to corporate VPN, you are well protected. The moment you go off, you are no longer protected. Your laptop is now open to being hacked and attacked at public wifi locations like airports, hotels, coffee shops, parks etc. My proposed solution involves an automated proactive cloud based threat intelligence platform that will not just monitor and detect threats in real time attacking you while at a public location but also when you are at home. The system works on Zero trust framework where there are no trusted networks or zones. Each system with an IP address has its own Intrusion Detection and Prevention System, combined with special localized malware analysis that is specifically targeting you.Most Anti Virus and anti malware companies, do not write their own signatures. Infact they buy them from smaller companies, my proposed solution will analyze malware targeted at you specifically and create a defensive signature within minutes to neutralize and eradicate threats against you within an hour across your entire infrastructure. There will be no need to wait 2–3 days for Anti virus and anti malware companies to come up with signatures and offer you protection.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".