Bibliographic record
Abstract
The operating system is a general program which manages a variety of hardware and software resources.Most operating systems such as Windows, Linux and, etc. are usually designed graphically.Of course, these have the advantages and disadvantages, the user can choose each of these operating systems according to your type of usage.The most important and most used operating systems are Windows and Linux which are used in the various applications.Today, in the various industries, the fast data processing has become an important problem in the application software such as ANSYS and etc.It may take days or weeks to process a large model in the ANSYS software which reduces the efficiency.The general idea of this research is to select the best operating system which has the best performance and speed in executing ANSYS software files.For this purpose, first, ANSYS fluent software executes on Windows 7 and Linux CentOS 7 operating systems with the same hardware and four processing files run in it and then, the software performance is compared in the serial and parallel modes in both operating systems and finally, the best operating system is selected which has the highest performance and speed in executing ANSYS software files after comparing.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.933 | 0.938 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".