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 computing technologies have made it possible for researchers to get access to more computational resources than ever before. However, heterogeneity of resources, scheduling policy and applications makes it difficult to manage the execution of jobs in such environments. The manner in which a job is configured to run on one resource may not be appropriate for another resource. Long running jobs need to be split into a series of shorter jobs with the aid of checkpointing to increase fault tolerance and to meet scheduling policy constraints of different resources. To take full advantage of grid environments, execution management systems need to be able to configure, reconfigure, checkpoint and migrate jobs as necessary. Information models that describe resources, policy and applications help automate execution management and allow for interoperability between tools. This paper presents application and job models that describe the processes required to configure and execute jobs on different resources. Example instances of the application and job models for the GROMACS molecular dynamics simulation example are also presented, illustrating how the models aid in automation.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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