Bibliographic record
Abstract
Allah has subjugated the universe for human beings and has attracted them to reveal the hidden secrets of the universe by using the abilities given by their Creator and Owner. In the present era, the knowledge of science and modern technology has become very important to increase the military power, and the nations that are experts in these sciences are superpowers in the world, and the nations that are behind in these sciences are weak and subdued. One of the reasons for the decline of Muslims in modern times is that they are lagging behind other nations in scientific and technological sciences, due to which they are weak compared to other nations in terms of military strength. This research paper explains the need and importance of acquiring scientific and technological knowledge to increase, strengthen and stabilize the military power among Muslims. It has been concluded from the research that acquiring knowledge of science and technology is the most important, advanced and best worship for Muslims to make the military force strong and stable for their security and to live with freedom, honor and dignity in the world. At the end of the research paper, suggestions have been made that Islam wants to see Muslims exalted in the world, so it is important for Muslims to become the cause of the exaltation of Islam by acquiring expertise in scientific and technological sciences and dominate Islam in the whole world and become exalted yourself.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.978 | 0.976 |
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".