Bibliographic record
Abstract
Distributed-system observation tools require an efficient data structure to store and query the partial-order of execution. Such data structures typically use vector timestamps to efficiently answer precedence queries. Many current vector-timestamp algorithms either have a poor time/space complexity tradeoff or are static. This limits the scalability of such observation tools. The self-organizing hierarchical cluster timestamp, introduced by Ward and Taylor, potentially has a good time/space tradeoff provided that the clusters accurately capture communication locality. However, the problem of accurately capturing communication locality has not been adequately addressed. In particular, the only clustering algorithm for which results have been presented is the merge-onfirst-communication approach. That strategy has limited applicability, as it is very sensitive to the order of event processing and to the maximum cluster size permitted. In this paper we evaluate alternate clustering strategies. We first studied a simple static clustering algorithm. This was chosen to confirm the basic premise of cluster timestamps, namely that good clustering will yield significant space saving. We then assessed the merge-on-Nth-communication approach, as a dynamic alternative to mergeon-first-communication. We present detailed results for the strategies evaluated, and offer recommendations for future work in clusteralgorithm selection for cluster timestamps. I.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".