Bibliographic record
Abstract
Being benefited by the favors of Allah Ta'ala, man is eager to find more facilities for himself every time and is busy day and night to make every area of life easier. But it is also surprising that on the one side, man wants facilities for himself, while on the other side, sometimes these facilities are the cause of death. As in earlier times people used to travel on Donkey Carts, Camels etc. Then times changed and engines started being used, people started traveling in cars. Development progressed until the time of the airplane came. People traveled the journey of month in hours. But along with this accidents also started to increase. Accidents in the past caused very few deaths, while today's accidents cause many deaths or at least maiming and injuring the human body or causing financial loss. After which the parties file various claims against each other. Therefore, keeping in mind the Islamic principle, it is necessary to resort to Sharia law to solve these problems. Because nowadays traffic usage is very high due to the abundance of people and it is increasing day by day. Due to which traffic problems are also faced more. Therefore, all countries and states issue orders for the traffic problems like all others problems of the country. For this reason, the Wali Swat also issued some orders for the residents of his successful state. Six of these orders are mentioned in the Riwaj Nama Swat, which are being reviewed in the light of Sharia in this article.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.974 | 0.980 |
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".