Using Docker, an Industry Standard Technology to Run GATE Simulation on Multiple Platforms
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
Simulation is mandatory to initiate any new high energy physics or medical imaging experiments. These simulations generally consume a lot of computational resources. Parallelism on large clusters of computers is among the most efficient strategies to reduce simulation time [1] , [2] . These clusters are not easy to put in place since they are very expensive and power hungry. Furthermore, creating the environment to simulate on these clusters takes time because of the lack of high privilege access on the machines. Finally, because of the specificity of each computer grid, it is also difficult to share simulation environment and methodology between research groups. Nowadays companies like Amazon, Google, and Microsoft propose affordable cloud computing resources. In addition to those resources, containerization software like Docker enable the description of an environment in a simple text file called Docker image consequently easing the set-up and sharing of a simulation environment.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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