K42
Bibliographic record
Abstract
K42 is an open-source scalable research operating system well suited to support systems research. The primary goals of K42's design that support such research include flexibility to allow a multitude of policies and implementations to be supported simultaneously, extensibility to allow new policies and implementations to be readily added, and scalability to enable good performance for both small and large applications on both small and large multiprocessor systems. The goals are accomplished via key features including an object-oriented structure that allows specialized resource management implementations and policies on a per-resource, per-application basis, implementation in user-level servers of much of the system functionality, and a sophisticated set of underlying services that provides a programming model for developing system software in a scalable and modular fashion.These characteristics make K42 an attractive framework for prototyping new operating system ideas. In addition, K42 has a sophisticated performance monitoring infrastructure allowing a thorough understanding of new ideas to be gained. The above framework combined with a consistent emphasis on scalability makes K42 well suited for high-end computing initiatives. In this paper, we describe the structure of K42 which contributes to the advantageous prototyping environment, and demonstrate how to utilize it by describing ongoing research efforts.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.072 |
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".