Bibliographic record
Abstract
The dynamics of majority voting has always been of interest in the area of discrete dynamical systems. In recent years, there has been a growing interest on this process also in the distributed computing field, due to its links to fault-tolerance, reliability, and virus disinfection. In fact, local voting mechanisms are often employed in distributed systems and networks as a decision tool for a variety of applications. In presence of faults, these schemes can trigger a dynamics of contamination: a non-faulty node will exhibit a faulty behavior if the majority of its neighbors is faulty. Some distributed and networked systems employ mechanisms to mend the faults; in these cases a decontamination dynamics is present and interacts with the contamination process. Depending on whether the decontamination is carried out by the majority-voting mechanism already in place or by the use of a team of mobile agents, the decontamination process is called internal or external, respectively. In this paper we focus on the contamination and decontamination processes in majority based systems and we survey the recent results in presence of both internal and external decontamination.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".