Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Grid technology is developed to share data across many organizations in different geographical locations. The idea of replication is to store data into different locations to improve data access performance. When different sites hold replicas, there are significant benefits realized when selecting the best replica. Current research shows that both network bandwidth and disk I/O plays major role in file transfer. In this paper, we describe a new optimization technique that considers both disk throughput and network latencies when selecting the best replica. Previous history of data transfer can help in predicting the best site that can hold replica. The k-nearest neighbor rule is one such predictive technique. In this technique, when a new request arrives for the best replica, it looks at all previous data to find a subset of previous file requests that are similar to it and uses them to predict the best site that can hold the replica. In this work, we implement and test k-nearest algorithm for various file access patterns and compare results with the traditional replica catalog based model. The results demonstrate that our model outperforms the traditional model for sequential and unitary random file access requests.
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.
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 | 0.000 |
| 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 it