Bibliographic record
Abstract
A replicação Máquina de Estados é uma das abordagens mais usadas na implementação de sistemas tolerantes a falhas, tanto por parada quanto bizantinas. Esta abordagem consiste em replicar os servidores e coordenar as interações entre os clientes e as réplicas dos servidores, com o intuito de que as várias réplicas apresentem a mesma evolução em seus estados. Para isso, as requisições dos clientes devem ser ordenadas e executadas seguindo esta ordem em todas as replicas. Este requisito fez com que a maioria dos trabalhos utilizassem uma única thread de execução em cada réplica. Com o objetivo de melhorar o desempenho do sistema, novas abordagens foram introduzidas para suportar varias threads de execução por réplica. Dando seguimento a estes trabalhos, este artigo descreve como um protocolo que possibilita o emprego de varias threads de execução nas réplicas foi adaptado e implementado no BFT-SMART, além de analisar uma série de experimentos realizados.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".