e‐Science Central for CARMEN: science as a service
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
Abstract Scientists face many severe challenges in extracting value from the increasingly large volumes of data they generate. In this paper we describe the requirements we have derived from working across a wide range of e‐science projects. In particular, the CARMEN neuroinformatics project has exposed a range of challenges due to a need to analyse and share large volumes of data. We have identified the four key activities required by scientists with whom we work, and designed an integrated system—e‐Science Central—to provide them. This exploits three emerging technologies: software as a service to avoid the need for users to deploy and maintain any of their own software; social networking to allow users to collaborate by sharing data, services and workflows in a controlled manner and Cloud computing to provide scalable compute resources. The system can not only be used through any web browser, but also provides an API so that applications can build on the core functionality. We describe the requirements, and the design that flows from them. This includes data storage with in‐built versioning and signing, an in‐browser workflow editor and a job scheduling system that allows workflows to be run both on local ‘private’ clouds and the Microsoft Azure Cloud. Copyright © 2010 John Wiley & Sons, Ltd.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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