Bibliographic record
Abstract
For this study, we have chosen to work with Parallel Virtual File System (PVFS) from Clemson University; it is a distributed file system for Linux that implements wide stripping. First, we have programmed and calibrated a PVFS simulator; using this simulator, we demonstrated that PVFS has a very good scalability where the number of nodes in the cluster is smaller than a given threshold. This threshold actually corresponds to the saturation of the network bandwidth. To go beyond this limit, we propose to use wide striping and replication in the same file system. We also propose a new data distribution technique based upon "chained declustering", that warranties high availability and scalability. This also allow's the system to be improved with minimal cost, without service interruption and with a minimal degradation of service. In addition, the granularity of the file system is ideal: the size of the cluster can be adjusted to the needed performances with a precision of one node. Finally, we propose a complete architecture, using cluster of clusters, where the performances are not limited by the network performances. In order to validate our file system, we use the PVFS simulator where the improvements have been implemented. The results show that the performances of the system are close to the ideal case. Once the size of the origin cluster is well defined, the total number of nodes in the system is not limited any more, and the performances increase linearly. We have also simulated an upgrade of the system, in order to measure the perturbation caused by the update of the new nodes: it is minimal and can be controlled by priority mechanisms.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".