Bibliographic record
Abstract
A practical problem faced by users of metacomputers and computational grids is: If my computation can move from one system to another, how can I ensure that my data will still be available to my computation? Depending on the level of software, technical, and administrative support available, a data grid or a distributed file system would be reasonable solutions. However, it is not always possible (or practical) to have a diverse group of systems administrators agree to adopt a common infrastructure to support remote data access. Yet, having transparent access to any remote data is an important, practical capability. We have developed the Trellis File System (Trellis FS) to allow programs to access data files on any file system and on any host on a network that can be named by a Secure Copy Locator (SCL) or a Uniform Resource Locator (URL). Without requiring any new protocols or infrastructure, Trellis can be used on practically any POSIX-based system on the Internet. Read access, write access, sparse access, local caching of data, prefetching, and authentication are supported. Trellis is implemented as a user-level C library, which mimics the standard stream I/O functions, and is highly portable. Trellis is not a replacement for traditional file systems or data grids; it provides new capabilities by overlaying on top of other file systems, including grid-based file systems. And, by building upon an already-existing infrastructure (i.e., Secure Shell and Secure Copy), Trellis can be used in situations where a suitable data grid or distributed file system does not yet exist.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".