Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern distributed systems have greatly benefited from developments such as model-driven development, and architectural description languages. Abstract models of components ( e.g. , IDL) and models of interconnection ( e.g. , architectural description languages, or ADLs) provide important software engineering advantages, such as explicit design models, type-checked integration across machine and language boundaries (with generated marshaling and dispatch code), the possibility of third-party components, and automated verification of design artifacts. But, when distributed systems are enhanced to provide security features, many of these advantages do not apply. Security features are hand-written into almost every part of the system; there is no explicit component or architectural model, or separable "security component" security code fragments are scattered and tangled through the different distributed elements of the system, and are often reduced to communicating through lowest-common denominator fragments (like raw bytes) since they are not represented in the model.In this paper, we describe DISCOA, a proposed extension of our earlier work on DADO [23] to handle security features in distributed systems, using explicit architectural models with aspect-oriented extensions.
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.
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 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it