Orion
Bibliographic record
Abstract
We present a case study on how Georgia State University (GSU) has grown its active High Performance Computing (HPC) research community by 80% in 2015 over previous year, and how GSU is projected to double its active HPC research community for 2016 over 2015. In October 2015, GSU launched an institutional HPC resource, Orion, which provides batch and interactive compute environment. Currently, Orion supports both the traditional and non-traditional research communities on our campus as well as our affiliates from Qatar University, University of Toronto, and Georgia Tech. At GSU, Research Solutions' HPC facilitators are responsible for facilitating the HPC research, which is done in a form of providing technical support in developing pipelines and automating job submission process for various applications that researchers need for their research. This approach has resulted in nearly 80% growth in our active HPC users from 2014 to 2015, and currently we are tracking at doubling our active HPC user community in 2016. XSEDE remains a backbone of our ambitious goals, as we rely on XSEDE for providing us the necessary resources for select users whose research quickly exceeds our local infrastructure.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.143 | 0.093 |
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".