Verifying the forecast: how climate models are developed and tested (invited talk)
Bibliographic record
Abstract
Stolen passwords, compromised medical records, taking the internet out through video cameras– cybersecurity breaches are in the news every day. Despite all this, the practice of cybersecurity today is generally reactive rather than proactive. That is, rather than improving their defenses in advance, organizations react to attacks once they have occurred by patching the individual vulnerabilities that led to those attacks. Researchers engineer solutions to the latest form of attack. What we need, instead, are scientifically founded design principles for building in security mechanisms from the beginning, giving protection against broad classes of attacks. Through scientific measurement, we can improve our ability to make decisions that are evidence-based, proactive, and long-sighted. Recognizing these needs, the US National Security Agency (NSA) devised a new framework for collaborative research, the “Lablet” structure, with the intent to more aggressively advance the science of cybersecurity. A key motivation was to catalyze a shift in relevant areas towards a more organized and cohesive scientific community. The NSA named Carnegie Mellon University, North Carolina State University, and the University of Illinois – Urbana Champaign its initial Lablets in 2011, and added the University of Maryland in 2014.
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 | 0.019 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".