Bibliographic record
Abstract
One of the lasting challenges in building distributed fault tolerant systems is keeping application code size and complexity down. This can be done by capturing the nuances of distributed computing environment and redundant fault tolerant elements into a common infrastructure layer, thus factoring the code that would otherwise need to be written again and again by each distributed fault tolerant software component. When the application code has many complexities, and Air Traffic Control (ATC) is certainly one such example, achieving this goal becomes paramount.Under a project called En Route Automation Modernization (ERAM), the Federal Aviation Administration (FAA) is developing a replacement for its aging en route assets. At the same time, a foundation is being created for the anticipated future enhancements, driven by the projected increase in air traffic. At the core of the ERAM design is a distributed object oriented (OO) framework called Publisher FrameWork (PFW), which is ERAM's answer to the aforementioned OO challenge. This paper describes the PFW properties, the experiences with it accumulated through the first build of the ERAM program, and its applicability to fault tolerant computing.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".